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Location: San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role Anthropic's research organization works across the full model development lifecycle, from pre-training and post-training to alignment, interpretability, and safety, each operating at the frontier of AI development. As a Technical Program Manager for Research, you'll define and build the programs that research teams need most. You'll move across research areas like compute, evals, RL environments, and emerging research initiatives, going deep enough in each to understand how researchers work and what they need. You'll identify where the biggest opportunities for impact lie, find the highest-leverage gaps, and build the programs, processes, and tooling that allow researchers to focus on research. This is a 0-to-1 role: you'll explore new domains as priorities shift, determine what each one needs, and create lasting impact where none existed before. Note: This role may require responding to incidents on short-notice, including on weekends. Responsibilities Embed deeply within a research domain to understand the technical landscape, build trust with researchers and technical leaders, and identify the highest-leverage problems to solve, knowing the surface area will shift over time as research priorities evolve Move fluidly across research areas like compute, evals, RL environments, and emerging research initiatives, picking up new domains quickly and getting to depth fast Drive end-to-end execution of complex, ambiguous research initiatives spanning multiple teams, often without established playbooks or precedent Establish processes and frameworks that bring structure to unstructured research environments without slowing researchers down Lead efforts like large-scale compute resource planning, including allocation, efficiency, and prioritization across research and production workstreams Drive eval readiness for model launches by standardizing results, shaping eval plans early, improving tooling, and ensuring honest, transparent reporting across research, product, and marketing Own execution and operational health of RL environments across major training runs, coordinating cross-team trade-offs and feeding insights back into roadmap planning Equip research leadership to make decisions quickly by going deep on technical tradeoffs and presenting clear, actionable recommendations Act as the connective tissue between research, engineering, and product teams to reduce chaos and accelerate execution You May Be a Good Fit If You Have a background in ML research or engineering with several years of experience building technical programs from scratch, ideally with hands-on exposure to training, evaluation, or large-scale distributed systems Are a fast learner who can ramp on unfamiliar technical domains quickly and contribute meaningfully to discussions with researchers Are resourceful, high-agency, and able to navigate ambiguity and shifting priorities to drive progress in fast-moving research environments Have a track record of operational ownership of complex technical systems, including monitoring, incident response, and performance optimization Can reason about technical tradeoffs at depth across model architecture, training infrastructure, evals, or compute efficiency, and translate them into clear decisions for leadership Have excellent stakeholder management skill and the ability to influence senior technical staff through competence and consistent delivery Are comfortable with high-stakes environments where decisions impact compute spend, model training timelines, and launch outcomes Are passionate about the potential impact of AI and are committed to developing safe and beneficial systems Are excited to redefine what technical program management looks like at the frontier of AI research The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $365,000 — $435,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: London, Singapore DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. The Machine Learning Researcher will have a deep understanding of the principles behind modern ML algorithms — including recent advances such as transformer-based architectures and other state-of-the-art frameworks — and the ability to turn that knowledge into high-impact, production-ready solutions. The role involves applying advanced ML techniques to a wide range of forecasting challenges, building scalable ML pipelines, and deploying them in production, while working with high-dimensional, noisy, structured and unstructured datasets. Experience applying ML models in financial markets is desirable, but exceptional candidates with a strong ML background from tech, startups, or academia will also be considered. Responsibilities Research, design, and deploy robust ML models. Build and maintain scalable, production-level ML pipelines. Extract signals from large, noisy, real-world datasets. Qualifications PhD (or exceptional MSc) in ML, Computer Science, or related field. Deep theoretical and practical knowledge of core ML algorithms, and comfortable experimenting with model architectures, feature engineering, and hyperparameter tuning to produce high-performance and resilient models. Proven experience taking ML models from research to live production. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . [#LI-AC]

Location: London, Singapore DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. We are seeking a Quantitative Researcher to work on alpha generation, model development, and the full research pipeline for mid-frequency equity statistical arbitrage strategies. The position will collaborate closely with the Portfolio Manager and other team members, contributing across all stages — from data processing and feature engineering to backtesting and live deployment. Key Responsibilities Research, design, and implement predictive signals and features across global equities. Work with large, diverse datasets; develop robust feature engineering and point in time (PIT) data cleaning pipelines. Collaborate with team members to implement research infrastructure, backtesting frameworks, and analytical tools. Build frameworks and tools for performance attribution and ongoing live strategy monitoring. Requirements 2–8 years of experience in quant equities, with a primary focus on alpha research. Familiarity with both standard and alternative datasets used in equity stat arb strategies, including data cleansing, ticker mapping, point-in-time (PIT) handling, and other dataset idiosyncrasies. Strong programming skills in Python; experience with SQL and distributed data environments. Advanced degree (PhD/MSc) in Mathematics, Physics, Statistics, Computer Science, or related quantitative discipline. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . [#LI-AC]

Location: San Francisco, CA | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Anthropic's production models undergo sophisticated post-training processes to enhance their capabilities, alignment, and safety. Decisions in production training depend on knowing how the model is really behaving. Our team's mission is to have the best observability into production post-training runs at Anthropic: to be the clearest, most trusted view of model quality during training. We shape what gets measured, keep the signal trustworthy and timely, and get to the bottom of surprising results to inform post-training and release decisions. In this role you will lead the science of how we evaluate production training runs. You'll work out which measurements tell us something real about the model, notice when they stop doing so, and find what we should be measuring but aren't. You'll partner with research teams across every RL domain, bringing their priorities into what we measure and setting the standard for what makes an eval trustworthy across post-training. You'll also be hands-on with the eval fleet day to day, because the best questions about measurement come from watching real results come in during a live run. Responsibilities Steer the eval strategy for production Claude models: study and optimize the eval mix so it gives the most accurate and complete assessment of model quality Run and monitor the eval fleet live to understand how each Claude model is developing as it trains Advise teams across post-training on eval methodology, and help eval authors bring new evals up to the bar for production Investigate regressions in production runs and inform training interventions when appropriate Build the dashboards, alerts, and reports that researchers and leadership use to track model quality You may be a good fit if you Have designed, run, and analyzed evaluations for ML models at scale Care deeply about measurement quality, thinking twice before trusting a number Can turn ambiguous results into clear recommendations, and are comfortable influencing direction across teams Have strong Python skills and are comfortable working with production systems Maintain clarity and rigor when debugging complex, time-sensitive issues Thrive in controlled chaos and are energized, rather than overwhelmed, when juggling multiple urgent priorities during a live training run Care about the societal impacts of your work and about shipping frontier models responsibly Strong candidates may also have Hands-on experience post-training large language models Research experience in ML evaluation or benchmarking Background in statistics and experimental design Experience developing robust evaluation metrics for ML systems The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Mumbai WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. The Role: WorldQuant is seeking an exceptional individual to join as Quantitative Researcher (Deep Research). Research is at the core of WorldQuant. Through rigorous exploration and unconstrained thinking about how to apply data to the financial markets, our researchers are in constant search of new alphas. Researchers at WorldQuant employ tested processes seeking to identify high-quality predictive signals that we believe are undiscovered by the wider market. These signals are mathematical expressions of data that are used as inputs in our quantitative models. As part of this role, you will: Build predictive signals using a structured and methodical research process combining fundamental knowledge, data exploration and quantitative analysis Become a domain expert on each fundamental topic you cover, identify key information drivers to target your research Stay updated on the latest advancements in AI and LLM research, identifying opportunities to integrate them into quantitative finance applications What You’ll Bring: Bachelor's (Hons), Master's or PhD degree in in a highly quantitative or highly analytical field e.g. Math, Physics, Computer Science or Financial Engineering from a leading university with excellent academic records are strongly preferred At least 2 years of relevant financial research experience Creative problem solver with a passion for experimentation and tackling unsolved challenges Strong foundation in mathematics, statistics, and quantitative modeling Strong interest in financial markets Proficiency in Python and C++ Knowledge and hands-on experience in AI, Machine Learning (ML), and LLMs is a plus Excellent communications skills (both written and oral English) By submitting this application, you acknowledge and consent to terms of the WorldQuant Privacy Policy. The privacy policy offers an explanation of how and why your data will be collected, how it will be used and disclosed, how it will be retained and secured, and what legal rights are associated with that data (including the rights of access, correction, and deletion). The policy also describes legal and contractual limitations on these rights. The specific rights and obligations of individuals living and working in different areas may vary by jurisdiction. Copyright © 2025 WorldQuant, LLC. All Rights Reserved. WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.

Location: London, UK About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: £370,000 — £630,000 GBP Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Remote-Friendly (Travel-Required) | San Francisco, CA | New York City, NY About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run. Responsibilities Build and improve the RL training infrastructure that researchers depend on day-to-day Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster Own the reliability and performance of research runs end-to-end Contribute to design decisions that shape how Anthropic does RL at scale You may be a good fit if you Have strong software engineering fundamentals and a track record of building performant, reliable systems Have worked on ML infrastructure, distributed systems, or research tooling Care about enabling other people's work and find leverage through platforms rather than individual experiments Are comfortable operating across the stack, from low-level performance work to RL algorithms Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego Strong candidates may also have Experience with large-scale distributed training (RL, pre-training, or post-training) Familiarity with JAX, PyTorch, or similar ML frameworks A track record of operating at the edge of research and infra in a fast-moving environment Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $500,000 — $850,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

О ПРОЕКТЕ Фаундер с 20-летним опытом ищет «правую руку» по исследованиям. Создается self-hosted ПО в сфере криптообменников, кошельков и процессинга. Кроме финтеха, в орбите личные проекты фаундера: AI-слой, Wellness и Biohacking. Требуется человек, который превратит интуицию в твердые данные. Задачи: — Advanced JTBD: глубокое исследование рынков с использованием всех артефактов методологии. — Global Research: поиск и интервью с англоговорящими клиентами (Африка, Северная Америка, ЮВА). — Product Launch: вывод внутренних продуктов (кошелек, процессинг) в публичный доступ. — AI-Driven: использование Claude Code в ежедневной рутине для анализа и ресерча. — Cross-Domain: переключение между крипто-сервисами и проектами в сфере Clinical Longevity. Требования: — Опыт 2+ года в продуктовой роли с акцентом на исследования. — Активное применение Job Stories и интервью на реальных продуктах. — Свободный английский для качественных созвонов. — Полная самоорганизация, доведение задач до результата без микроменеджмента. — Понимание основ крипты (кошельки, обменники) и интерес к AI и биохакингу. Условия: — Первый месяц full-time (притирка), далее по объему задач. — Оформление по договору услуг + NDA. — Таймзона: МСК ±3 часа. — Возможность оплачиваемого тестового задания после созвона.

Location: Remote Workplace: Remote We are building AI to simulate the world through merging art and science. We believe that world models are at the frontier of progress in artificial intelligence. Language models alone won’t solve the world’s hardest problems – robotics, disease, scientific discovery. Real progress requires models that experience the world and learn from their mistakes, the same way that humans do. And this kind of trial and error can be massively accelerated when done in simulation, rather than in the real world. World models offer the most clear path to general-purpose simulation, changing how stories are told, how scientific progress is made and how the next frontiers of humanity are reached. Our team consists of creative, open minded, caring and ambitious people who are determined to change the world. We aspire to continuously build impossible things and our ability to do so relies on building an incredible team. If you are driven to do the same, we'd love to hear from you. *Titles posted to our careers page are updated regularly to best advertise the role and expected seniority, but internally we have a flat titling system with two types of titles: one for individual contributors and one for people managers. ABOUT THE ROLE *Open to hiring remote across the US — preferable to have someone near an office in NYC or San Francisco. This is a rare PM role with true vertical ownership across the full product lifecycle; from early-stage research and pre-training through to post-training and UX buildout through to GTM strategy and pricing - giving the right person a GM-like mandate to drive product decisions end-to-end in a way that's unique for a company of our scale. We’re looking for a passionate Product Manager to help us develop cutting-edge AI tools. This role is for someone who has a combination of strong technical or ML background and experience in product management. You have the ability to take the state-of-the-art AI systems our research team builds and turn them into impactful tools for our customers. You’ll deeply understand what users want, what’s possible to build, and what’s strategic for our business. You’ll be a bridge between our users and our internal teams so we ship products that have the most value for Runway’s creators. This role is a fundamental and critical part of the team for a market-defining company in its early stages. WHAT YOU’LL DO - Work alongside our research team to define new AI applications that solve customer needs and drive business value - Collaborate closely with engineering and design to productize research and continuously improve existing tools - Define and analyze metrics, talk with users weekly, and conduct A/B tests to discover use cases, validate pain points, and prioritize features WHAT YOU’LL NEED - 1+ years of direct experience with SQL, python, code generation tools, and version control best practices - 4+ years of product management experience - Familiarity with the current state-of-the-art ML capabilities and ML products - Experience working with fast-moving teams and building products from scratch - Passion for seeing research through from initial conception to ultimate application - Excellent written and verbal communication skills Runway strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on competitive market rates for our size, stage and industry, and salary is just one part of the overall compensation package we provide. There are many factors that go into salary determinations, including relevant experience, skill level and qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range. Lastly, the provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range, which again, will be communicated to candidates. WORKING AT RUNWAY Great things come from great teams. https://www.youtube.com/watch?v=kwmj4ato2kw&ab_channel=Runway We’d love to hear from you. We’re committed to creating a space where our employees can bring their full selves to work and have equal opportunity to succeed. So regardless of race, gender identity or expression, sexual orientation, religion, origin, ability, age, veteran status, if joining this mission speaks to you, we encourage you to apply. More about Runway - Universal World Simulator https://runwayml.com/world-simulator.html - GWM-1 https://runwayml.com/research/introducing-runway-gwm-1 - Gen-4.5 https://runwayml.com/research/introducing-runway-gen-4.5 - General World Models https://runwayml.com/research/introducing-general-world-models - Robotics SDK https://runwayml.com/research/introducing-runway-gwm-1#robotics-section - Conversational Real-time Agents https://runwayml.com/research/introducing-runway-gwm-1#avatars-section - Runway Studios https://runwayml.com/studios We're excited to be recognized as a best place to work: Crain's https://www.crainsnewyork.com/awards/best-places-work-2023 | InHerSight https://www.inhersight.com/companies/best/city/new-york-city-ny | BuiltIn NYC https://builtin.com/awards/new-york-city/2024/best-places-to-work | INC https://www.inc.com/best-workplaces/2024

Location: Mumbai WorldQuant develops and deploys systematic financial strategies across a broad range of asset classes and global markets. We seek to produce high-quality predictive signals (alphas) through our proprietary research platform to employ financial strategies focused on market inefficiencies. Our teams work collaboratively to drive the production of alphas and financial strategies – the foundation of a balanced, global investment platform. WorldQuant is built on a culture that pairs academic sensibility with accountability for results. Employees are encouraged to think openly about problems, balancing intellectualism and practicality. Excellent ideas come from anyone, anywhere. Employees are encouraged to challenge conventional thinking and possess an attitude of continuous improvement. Our goal is to hire the best and the brightest. We value intellectual horsepower first and foremost, and people who demonstrate an outstanding talent. There is no roadmap to future success, so we need people who can help us build it. The Role: Research is at the core of WorldQuant. Through rigorous exploration and unconstrained thinking about how to apply data to the financial markets, our researchers are in constant search of new alphas. Researchers at WorldQuant employ tested processes seeking to identify high-quality predictive signals that we believe are undiscovered by the wider market. These signals are mathematical expressions of data that are used as inputs in our quantitative models. WorldQuant is seeking an exceptional individual to join the firm as a Quantitative Researcher. The person must have a strong understanding of the investment research process to create computer-based models that seek to predict movements of global financial markets. While prior finance experience is not required, a successful candidate must possess a strong interest in learning about finance and global markets. Candidates will have a research scientist mind-set; be a self-starter, a creative and persevering deep thinker who is motivated by unsolved challenges. WHAT YOU’LL BRING: Bachelor's (Hons), Master's or PhD degree in in a highly quantitative or highly analytical field e.g. Math, Physics, Computer Science or Financial Engineering from a leading university with excellent academic records are strongly preferred Have a research scientist mindset, i.e., be a deep thinker, creative, persevering, smart, a self-starter, etc. Knowledge of Linear Algebra, Statistics, Machine Learning Prior experience in Finance/Trading is a plus. Be competent in a programming language (Python, Unix) Possess good English language skills Have a strong interest in learning about worldwide financial markets Possess a relentless drive to succeed, supplemented by a strong work ethic By submitting this application, you acknowledge and consent to terms of the WorldQuant Privacy Policy. The privacy policy offers an explanation of how and why your data will be collected, how it will be used and disclosed, how it will be retained and secured, and what legal rights are associated with that data (including the rights of access, correction, and deletion). The policy also describes legal and contractual limitations on these rights. The specific rights and obligations of individuals living and working in different areas may vary by jurisdiction. Copyright © 2025 WorldQuant, LLC. All Rights Reserved. WorldQuant is an equal opportunity employer and does not discriminate in hiring on the basis of race, color, creed, religion, sex, sexual orientation or preference, age, marital status, citizenship, national origin, disability, military status, genetic predisposition or carrier status, or any other protected characteristic as established by applicable law.

Location: San Francisco Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. We are looking for an engineer with strong experience in machine learning and solid foundations in math and computer science to join our growing Post-Training team at Baseten. THE ROLE Custom models are instrumental to the success of Baseten customers. By inference volume, the overwhelming majority of traffic at Baseten is to and from models that have been post-trained in some way, whether that be through reinforcement learning, supervised finetuning, a recent technique from the literature, or an in-house research technique from Baseten. The Post-Training team is responsible for the success of our customers’ post-trained models, and we employ a wide array of techniques to produce models that are more efficient and higher quality than even the biggest closed source models for the customer’s specific needs. Your role as a research engineer is to build the in-house tooling to support all of this. We care about training a wide spectrum of different model architectures with a variety of techniques efficiently and at scale. At times this involves zooming deep into a particular technical topic, but more often it involves working across the stack as a whole - systems-level concepts like Kubernetes, cgroups, storage systems, and networking topologies, as well as PyTorch distributed tensor computation, and GPU kernels. The Manifesto: https://labs.baseten.co/manifesto RECENT RESEARCH - Dense, on-policy or both? https://www.baseten.co/research/dense-on-policy-or-both/ - Repeated kv cache for long-running agents https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/ - Distillation without the dark – replicating black-box on-policy distillation on Baseten https://www.baseten.co/research/distillation-without-the-dark/ We don’t have a rigid set of skills, but here’s some of what we’re looking for: - A deep understanding of modern ML techniques and tools for training transformers - Advanced experience in a tensor/array computation library like PyTorch, TensorFlow, Jax, or similar - A detailed understanding of transformer training parallelism strategies like data parallelism, sharded data parallelism, tensor parallelism, pipeline parallelism, context parallelism - The experience and knowledge to profile and improve the performance of a distributed GPU program in PyTorch or a similar library - The ability to perform roofline analysis on a transformer training setup - A willingness to dive into messy problems, work with researchers, derive specifications by asking important questions, and execute - Familiarity with HPC and distributed computing platforms like Slurm, Ray, Kubernetes, and Dask - Familiarity with cluster networking technology like Infiniband, RoCE, GPUDirect - Solid fundamentals in operating systems concepts like processes, files, kernel drivers, containerization, and networking protocols - A sense of creativity and willingness to ask difficult questions about our approach, assumptions, and tooling choices BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: United States + International (Remote) GiveWell’s mission is to help people in need as much as we can by researching the most cost‑effective ways to save and improve lives, sharing our work openly, and directing donations to the programs we believe will do the most good. Our work, which focuses on global health and well-being, is funded by tens of thousands of donors who rely on our research to inform their giving. We’ve grown from directing $1.5 million in 2010 to directing more than $400 million in 2025. We anticipate our total grantmaking is likely to approach or even exceed $1 billion this year, and we are working toward a future that could be several times that scale. Through years of deliberate groundwork, we’ve been growing our research capacity and scope in order to direct substantially more funding to the most impactful opportunities we can find. Summary GiveWell's senior research staff are the key decision makers for hundreds of millions of dollars in grantmaking, and we think growing that team is one of the most important things we can do to increase our impact. We're seeking one or more Senior Recruiters to help us do that. Finding senior research staff is a hard problem for a couple reasons: The talent pool for GiveWell researchers basically doesn’t exist as a defined group. You can’t go to one type of program, industry, or a few obvious competitors to build a reliable pipeline. The people who succeed here come from a wide range of backgrounds — economics PhDs, policy analysts, management consultants, medical doctors, neuroscientists, primate biologists, etc. — and the roles don't have clean analogues elsewhere. What they share is a set of hard-to-measure cognitive skills and quantitative fluency that doesn’t show up reliably on resumes. Our process is very competitive. The offer rate for senior research roles is less than one in a thousand. We want you to figure out where to find strong candidates, improve our assessment process, and do the hands-on work of sourcing, screening, and closing offers. You'll work very closely with our research leadership team. We're most excited about finding someone who helps us make better decisions about finding great talent — and hire them faster. The role You'd work closely with our manager, research recruiting and the team to own recruiting for some of our Researcher , Program Officer, Senior Researcher , and Senior Program Officer roles. A few problems we’ll want you to think about: After candidates apply, is our assessment process effective? Our offer rate for senior roles is below 0.1%. That might mean we’re extremely selective. It might also mean we’re losing good candidates somewhere in the funnel. You’ll dig into our data and find answers (How well do early screening scores predict work trial performance? What are the most common rejection reasons, and are they signal or noise? How did past hires actually turn out?). You’ll help us develop good heuristics about what to do in situations when candidates’ signals are mixed. Where do we even find good candidates? The ideal GiveWell researcher doesn’t come from one degree program, industry, or competitor. You’ll think about where to look and go after them. And you’ll continually develop new pipelines with creative methods (running structured referral conversations with our researchers, scouring conference guest lists, using sourcing tools creatively, or maybe vibe-coding your own). How can we use data to make better decisions? Right now, we don’t have great visibility into which sourcing channels, process changes, or pipeline stages are driving results. You’ll track data, run analyses, and help us make evidence-based decisions about where to put effort. In some cases, you might find out that we don’t have the right data architecture to answer important questions, so you’ll build it. What are ways our hiring process could be much better? This is harder to describe because it covers ~every strategic and tactical question we face. We want you to be thinking about all of it. Things like: Are we overweighting particular qualities and losing promising candidates without noticing? How could we run experiments on our hiring processes (randomization, A/B testing, sourcing campaigns) that actually give us useful information? You’ll also do a fair amount of bread-and-butter recruiting work: screening applications, writing outreach emails, and interviewing candidates. We think that sort of work will keep you close to the data and help you develop strong pattern recognition. About you You’re excited about GiveWell's mission and methods. You think GiveWell is doing something special, not just generically “doing good.” You can explain what makes GiveWell different. You’re careful and truth-seeking with your beliefs. For example, if a hiring manager says we need to find “scrappy” people, you’re likely to ask “Why do you believe that?” or “What does scrappy mean?” instead of uncritically agreeing. You’re quick to update your beliefs by picking up new information and asking sharper questions. You can probably tell us about times when you’ve changed your mind about important things. You're unusually curious and analytical. When you get invested in a question, you keep trying to find the right answer after many other people would have stopped. You ground your beliefs in data when possible and hedge appropriately when you’re not confident. You have very high standards. We have a high competence bar for our research team, and you’ll need to uphold that and own your decisions about applications that do and don’t meet our bar. You’re entrepreneurial and high agency. You figure things out. You have a history of solving problems. You’re very resourceful. You’re more likely to make the mistake of moving too fast than moving too slow. You'd rather try something imperfect and learn from it than wait for a perfect plan. You're direct. You say what you actually think even when it's uncomfortable. You’re good at communicating in writing. You're concise, clear, and you don't hide behind jargon. You probably enjoy an effective memo. If you think people who know you well would agree that many of the qualities above describe you, we’d like you to apply. Qualifications We care much more about the qualities described above than any specific background. That said, some things that might help: Experience in recruiting, talent operations, research operations, or a generalist role at a mission-driven organization Comfort with data — spreadsheets and basic quantitative analysis Strong written communication Experience working closely with executive-level leadership Familiarity with global health, development economics, effective altruism, or adjacent fields is a plus but not required Recruiting experience is welcome but not required. Some of the best people in comparable roles at peer organizations came in without it. And we’ll support you if you want to move on to other things at GiveWell after making progress on our research recruiting. There's always a place here for generalists who are good at solving hard problems, and we have a history of developing high performers into different roles across our org. Key Questions What are some of the reasons I might not like this role? GiveWell has a unique approach to hiring, and it’s possible that you’ll dislike it. For example, we tend to lean heavily into work trials and finding empirical evidence about the candidates we hire. We are rigorous in how we evaluate candidates, and hold really high standards, which can lead to longer hiring rounds with more steps than what is typical elsewhere. We’re extremely quality focused and would rather hold out for the right candidate than settle on someone who’s “mostly right.” You’ll need to deeply understand our work and culture to succeed. We want you to be able to speak with candidates about our work and what makes GiveWell special, and provide useful input to hiring managers when we’re developing assessments and evaluating candidates. You will be expected to hunt, not just farm. While we receive a high volume of inbound applicants, we are looking for recruiters who effectively source and attract candidates, not just wait for them to trickle in. You’ll be collaborating with leaders with high expectations. We’re a founder-led, performance-driven culture and you’ll be collaborating with our executive team regularly in this role. Strong partnerships will depend on you building trust, leading with curiosity, and reliably delivering results. You’ll be running hiring rounds from start to finish. We consider this a full-lifecycle role, which means you should be comfortable with things like scheduling, building out roles in Greenhouse, and day-to-day coordination with candidates. While you might have support from more junior colleagues, you’ll be expected to capably manage your own pipelines. What does growth look like in this role? GiveWell’s growing, and you’re going to help shape that. Your growth will be tied directly to the impact created by the people you help hire. Beyond day-to-day recruiting, you’ll have the opportunity to work on more strategic projects, including shaping our overall hiring approach and improving our employer brand. In this role you’ll also have significant exposure to executive leadership and many corners of our business. The talent acquisition team is doubling in 2026, which means there are clear opportunities for mentorship and potentially growing into a team leader. What does this interview process entail? We'll use a combination of interviews, written exercises, and work trials to assess the qualities described above. Past versions have looked like: Simulating a hiring manager intake conversation Walking through a sourcing strategy for a hard-to-fill role Reviewing a job description or process and offering a critique Writing a candidate outreach email or internal communication We'll share more specific details if we invite you to move forward, but our current plan is: Screen with a member of the Talent Acquisition team Career history interview with the Manager, Talent Acquisition Work trial, which will include live conversations and written work Values interview with VP, People Final conversation with Manager, Talent Acquisition References Team structure This role sits on GiveWell's People team and reports to our Manager of Talent Acquisition. Day-to-day, you'd work in close partnership with our Research Program Director, who owns senior research hiring decisions and is part of our Research Leadership Team. You'd collaborate regularly with other members of the People and Research teams on sourcing and candidate evaluation. We'll likely also want you to attend research team meetings, develop 1:1 relationships with research team members, and potentially join research team-building retreats. We’ll be interested in ~any kind of team embedding that helps you build useful context. The details Compensation: NYC or the San Francisco Bay Area: $149,600 All other U.S. locations: $136,000 Benefits: Our benefits include: Fully funded health, dental, vision, and life insurance (we cover 100% of premiums within the US for you and any dependents) Four weeks of paid time off per year 16 weeks of fully paid parental leave Ergonomic home workstations or coworking space memberships Automatic contribution equal to 5% of your gross salary into your 403(b) retirement plan (for U.S. based staff) Countries we can hire from: GiveWell is a remote-first organization. However, for the time being, we can only hire within a select number of countries (US, UK, Canada, Switzerland, and Germany). To be considered for a role with GiveWell, you need to already be based in one of those countries or be willing to move to one of them. We expect to expand the list of locations where we can hire over the course of 2027, and our job descriptions always reflect the current status — so we’d encourage you to check back and apply again for a role you’re eligible for. Visa sponsorship: We are not currently able to sponsor visas for this role. Flexibility: We support and encourage flexible working, including flexible hours, working remotely, and working from the office when you choose. The majority of our staff, including senior management, work flexibly in one way or another. Travel: GiveWell hosts “Visit Weeks” twice a year in our Oakland, California, office and up to two annual departmental retreats. Attending these in-person opportunities will be important to fulfilling the goals of this role. Occasional additional travel may be helpful for conferences or recruiting events. Start date: We’d like a candidate to start as soon as possible after receiving an offer, but we’ll offer flexibility for candidates whose personal or professional circumstances require them to moderately delay their start date. We don't want to miss candidates who could do great things at GiveWell. If you're on the fence about applying because you meet some but not 100% of our preferred qualifications, we encourage you to apply anyway. GiveWell is an Equal Employment Opportunity employer by choice. We value our team's diversity in all respects and desire to maintain a work environment free of harassment or discrimination. If you need assistance or an accommodation due to a disability, contact us at careers@givewell.org. About GiveWell GiveWell is dedicated to finding and evaluating highly cost-effective global health and development opportunities, and we publish the work, reasoning, and uncertainties behind our funding decisions. Our giving funds enable donors to contribute to the most impactful and cost-effective programs our researchers identify. GiveWell is one of the world’s largest private funders of global development efforts. Trusted annually by tens of thousands of donors, GiveWell searches for highly cost-effective programs that save or improve lives the most. Since 2007, more than 160,000 donors have given over $3.5 billion through GiveWell to the most promising programs its research has identified, which we estimate will save more than 430,000 lives . GiveWell is most well-known for recommending a small number of Top Charities , which currently support seasonal malaria chemoprevention , insecticide-treated nets , vaccine incentivization , and vitamin A supplementation . However, most of our research capacity and grantmaking is devoted to cost-effective opportunities outside of those programs . GiveWell grants have: Helped governments navigate changes to the funding landscape and implement high-impact health programs, like HIV/syphilis screening and treatment for pregnant people in Zambia and Cameroon. Sought to scope and scale promising interventions that don’t have clear existing implementers. We are supporting the Clinton Health Access Initiative’s Incubator to identify potentially cost-effective interventions and create programs that we would be excited to support in the future. For example, we funded a program to prevent tuberculosis among young children in Uttar Pradesh and Bihar, India, that we co-designed with CHAI through the Incubator program. Provided funding to ensure that programs that recently faced foreign aid funding cuts could continue, like healthcare support and malnutrition treatment in northern Cameroon. Funded program delivery alongside strengthened monitoring and evaluation, as in our grants to support treatment of clubfoot and to evaluate the program . Tested our assumptions and refined our understanding through further research and data collection, including improved estimates of disease burden and studies on the effect of water chlorination on mortality and the impact of a tree-planting program on farmers’ income . We make our reasoning public and transparent so others can challenge it (sometimes we even pay people to point out our errors ). We go to unusual lengths to check our assumptions and assess our impact, including funding research to address our uncertainties and gathering independent monitoring and evaluation data about the programs we support. We change our minds when the evidence demands it. For more about GiveWell, listen to Ezra Klein’s interview with GiveWell’s co-founder and CEO Elie Hassenfeld. Additional information We don’t want to miss candidates that could do great things at GiveWell. Practically, that means a GiveWell staff member reviews every application carefully, considering the whole picture of your background and potential. If you’re on the fence about applying because you meet some but not 100% of our preferred qualifications (some studies suggest this hesitation is especially common for women and people of color), we encourage you to apply anyway. GiveWell is an Equal Employment Opportunity employer by choice. At minimum, this means that we comply with all federal, state, and local EEO and employment laws. Beyond the requirements of those laws, we value our team’s diversity in all respects, and we desire to maintain a work environment free of harassment or discrimination—we want our team members to thrive at GiveWell. If you need assistance or an accommodation due to a disability, contact us at careers@givewell.org. We will consider employment for qualified applicants with arrest and conviction records. By submitting an application, you acknowledge that you have read and consent to GiveWell’s Privacy Statement for Applicants . By completing an application exercise, you acknowledge and assent to GiveWell’s Work Trial Policy .

Location: New York City DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. Cumberland Systematic is a multi-asset trading team focused on mid and high-frequency fully systematic strategies in the delta one space. We trade 24/7 globally with our team located primarily in Chicago and London. We are looking for a Software Engineer to join our team. You will work with other software engineers to design and develop the full stack needed to enable a global trading operation with high availability requirements. You will be expected to design and develop integrations with data vendors, blockchain infrastructure, data management solutions, and libraries for managing and analyzing data. The team’s existing systems are written in Python and C++. Candidates should have strong initiative and proven experience independently driving projects to completion. We work from high-level requirements and engineers are expected to gain an intimate understanding of the business and work directly with quantitative researchers on a daily basis. Responsibilities: Design, develop, test, document, and maintain software and infrastructure needed for quantitative financial research Accelerate the research lifecycle from data engineering to feature generation and analysis Integrate with diverse data sources such as blockchains, exchanges, and data vendors Test at the unit, functional, and integration levels Work within in a small team as well as independently with a strong user focus Provide on-call support as needed To qualify for this role, you must have: Python experience gained through professional experience Built complex systems in modern Python that are maintainable and safely refactorable Hands-on experience managing large, evolving timeseries data A working knowledge of type safety and type systems Strong verbal and written communication skills Strong internal motivation and a continual desire to learn Bonus points if you have: Contributions to open-source crypto projects Production experience running full or archive blockchain nodes at scale Collaborated with quantitative researchers A working knowledge of high-level machine learning concepts and lifecycle Experience with functional programming and immutable design principles Practical knowledge of major blockchain types and clients The annual base salary range for this position is $175,000 to $225,000 depending on the candidate’s experience, qualifications, and relevant skill set. The position is also eligible for an annual discretionary bonus. In addition, DRW offers a comprehensive suite of employee benefits including group medical, pharmacy, dental and vision insurance, 401k (with discretionary employer match), short and long-term disability, life and AD&D insurance, health savings accounts, and flexible spending accounts. For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . #LI-BL1

Location: Brazil About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . *This is a remote role and can be performed anywhere in Brazil/Colombia* The Role We are looking for a Research Engineer to help deliver frontier-quality datasets, RL environments, and evaluations that improve state-of-the-art models for a frontier AI lab client. You will work directly with the client's researchers and engineers, turning inbound requests and post-training goals into concrete technical proposals and data/environment specifications, and then owning the technical execution: building the quality and verification systems that ensure what we deliver meets extremely high standards for correctness, realism, diversity, difficulty, and measurable model lift. Most of this work is bespoke and custom, built for one client's evolving needs.You'll typically stay attached to a single frontier lab client so you can build real context and a working relationship with their team, with flexibility to support other accounts when needed. Because bespoke work is judged on both turnaround time and quality, both matter equally here. This role is designed for candidates with experience building and improving deep learning systems, especially where strong results depend on data quality, data curation, denoising, synthetic data generation, and rigorous evaluation. You'll operate in one or more of the following capability areas: Coding and software engineering agents (repositories, unit tests, debugging, tool use, code reviews, long-horizon workflows) RL environments and verifier-based training (tasks, rewards/verifiers, trajectories, evaluation harnesses) Multimodal data and reasoning (text + images + documents + tables/charts; optional audio/video) STEM reasoning (math, physics, chemistry, bio, engineering – solution verification and error analysis) Modern embodied AI / VLM-driven agents (vision-language(-action) models, embodied task suites, tool/sensor/action abstractions, long-horizon interaction data) What You'll Do 1) Own data and environment quality from an AI researcher perspective Respond to inbound requests from the client and translate ambiguous, evolving research goals into a technical proposal and clear data requirements: target skills, failure modes, difficulty calibration, coverage, and success metrics. Provide the technical feasibility assessment, assumptions, acceptance criteria, and evaluation requirements needed to scope the contract or statement of work. Define what “good” looks like by creating detailed rubrics, counterexamples, and boundary cases (what to include vs. exclude). Perform deep, detail-oriented audits of produced data: spot subtle errors, reward hacking opportunities, leakage, ambiguity, inconsistent assumptions, and distribution shifts. Drive iterative improvements using evidence: group failures (for example, by semantic similarity) to find the underlying gap, then turn that into concrete instructions for the production team — better few-shot examples, explicit counterexamples, clearer guidance on what's causing rejections. 2) Design and build datasets and RL environments for your capability area(s) Contribute to or lead the design of: Task suites (single-step and long-horizon workflows) Ground-truth signals (verifiers, unit tests, structured checks, reward functions, automatic validators) Environment interfaces (APIs, tool schemas, state abstractions, database schemas, simulator-like dynamics) Depending on your mapped capability area(s), you may focus on: Coding / SWE agents: data reflecting real development work (codebase navigation, bug localization, patching, tests, code reviews, CI-like constraints, refactors, security fixes). Multimodality: tasks that test true multimodal reasoning (chart reading, document QA, UI understanding, diagram-based STEM reasoning, OCR-aware tasks). STEM: tasks with verifiable solutions (symbolic checks, reference solvers, numerical validation, step consistency, unit sanity). Modern embodied AI / VLM-driven agents: interaction data and environments for vision-language(-action) models. 3) Build robust validation, denoising, and synthetic data systems Build project-specific verification systems that combine deterministic checks, model-based evaluators calibrated against gold sets, and targeted human review. Implement automated validation and filtering to achieve frontier-grade signal-to-noise: deduplication, decontamination, leakage checks, consistency checks, difficulty and diversity controls. Develop synthetic data generation and augmentation pipelines where appropriate: programmatic task generators, controlled perturbations, scenario templating, simulator-/tool-driven rollouts. Create documentation and data cards: dataset intent, known limitations, recommended use, and evaluation linkage. 4) Use evaluations and training runs to prove impact Design and run evals that reflect the client's intended usage. Produce analysis that connects data to outcomes: pre/post comparisons, error breakdowns, ablations that identify which data attributes drive lift. When needed, run in-house fine-tuning or RL-style experiments (or partner with research) to demonstrate that the data/environment improves model behavior in measurable ways. 5) Collaborate effectively with large production teams without being ops-heavy Give technical direction to the distributed expert network producing the data, by providing clear specs, examples, edge cases, and fast feedback loops based on audits and quantitative signals. You own technical translation, acceptance criteria, evaluator and verifier design, quality diagnosis, and the technical release recommendation. The Strategic Project Lead owns production execution, staffing, throughput, project cost, and recovery. You are expected to be highly engaged in reviewing and improving outputs from large annotation/creation efforts, but not primarily responsible for hiring, staffing, or people operations — that sits with the SPL. Communicate directly with the client on technical findings, quality risks, and recovery options. Align changes to scope, schedule, or external commitments with the account and delivery owners. Who We're Looking For 4–5 years of experience building or improving deep learning systems where data quality mattered materially (training, post-training, evals, or agentic systems). Strong intuition for the “data ingredients” that drive model improvements: what to collect, what to filter, what to synthesize, and how to measure. Ability to communicate clearly with researchers and engineers: turning research objectives into concrete specs, and turning messy outputs into actionable insights. Comfortable being the direct technical point of contact for a client, including explaining setbacks or trade-offs when things don't go as planned. Demonstrated ability to be extremely detail-oriented in diagnosing subtle data quality issues and failure modes. Solid programming ability with a bias for shipping: Python proficiency required; comfort with SQL/structured data workflows strongly preferred; for coding-focused work, proficiency in a major language (e.g., C++, Java, Go, Rust, JS/TS) is a plus. Comfort designing quality systems: rubrics, validation scripts, gold sets, sampling strategies, statistical checks, slice-based evaluation, human-in-the-loop review loops grounded in measurable criteria. Direct experience with a frontier AI lab is not required, and is uncommon in the LATAM market — what matters most is hands-on engineering depth and the ability to operate with ambiguity. Strong pluses RL or post-training experience (any of: RLHF/RLAIF, verifier training, reward modeling, RL fine-tuning, environment design). Experience with agentic evaluation (tool use, multi-step workflows, long-horizon tasks, trajectory analysis). Multimodal expertise (document understanding, charts, diagrams, OCR, UI/vision grounding; audio/video optional). STEM depth (math/physics/engineering) with an eye for verifiability and rigorous correctness. Modern embodied AI / VLM-driven agent experience (vision-language(-action) models, interaction datasets, embodied evals, long-horizon grounding, tool/sensor/action interfaces). Systems thinking: ability to “simulate” an application's API/data schema and design tasks that realistically reflect real-world constraints and workflows. Why Turing Work directly with a frontier AI lab, at the cutting edge of post-training and RL environment design, embedded in a real research relationship rather than a rotating set of anonymous tasks. Real impact (path to AGI): your datasets and environments will directly influence the trajectory toward Artificial General Intelligence and, ultimately, Superintelligence. High autonomy and fast iteration, with a Strategic Project Lead handling scaling and resourcing so you can stay focused on the technical approach and quality bar. Talent-dense team, where you'll find rapid iteration and an exceptional learning curve. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: San Francisco Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE This role sits at the frontier of our research agenda. You will pursue open problems at the intersection of post-training methodology and performant inference, and then collaborate with research engineering to translate findings into production systems. A meaningful portion of your time will be dedicated to research that deepens our understanding of how models learn, alignment, and architectural efficiency — questions that may not have immediate product application. The remainder will be directed toward research that solves concrete problems for Baseten's platform and customers, who are the fastest growing AI companies in the world like Cursor, Lovable, and Notion. We are looking for someone with sharp research taste and genuine creative instinct for problem selection. Someone who can identify questions that matter, design clean experiments to answer them, and push the state of the art. The environment here is not theoretical, but rather research that can be validated with eager customers who are serving billions of tokens a second. The Manifesto: https://labs.baseten.co/manifesto RECENT RESEARCH - Towards infinite context windows: neural KV cache compaction https://www.baseten.co/research/towards-infinite-context-windows-neural-kv-cache-compaction/ - Dense, on-policy or both? https://www.baseten.co/research/dense-on-policy-or-both/ - Repeated kv cache for long-running agents https://www.baseten.co/research/repeated-kv-cache-for-long-running-agents/ - Distillation without the dark – replicating black-box on-policy distillation on Baseten https://www.baseten.co/research/distillation-without-the-dark/ RESPONSIBILITIES - Define and pursue a research agenda spanning both foundational and applied work, with the applied component connected to Baseten's platform and customer needs. - Design and execute rigorous experiments, frequently at meaningful scale (multi-node, trillion parameter models). - Work with customers to translate domain-specific requirements into research problems, where relevant to your agenda. - Publish at top venues (NeurIPS, ICML, ICLR) and establish Baseten's research presence. - Collaborate with model performance and training infrastructure teams to bridge research findings and inference production systems. - Mentor junior researchers and shape the technical direction of the research organization as it grows. PREFERRED QUALIFICATIONS - Master’s or PhD research depth in machine learning, with first-author publications at top venues - Demonstrated ability to move from theory through implementation to empirical results — not exclusively theoretical or exclusively engineering work - Judgment about problem selection, the ability to distinguish research that advances a metric from research that changes how systems are built - Willingness to operate in a startup environment where the majority of research informs product decisions, with timelines measured in months rather than years - Background spanning multiple research areas (e.g., both interpretability and RL, or both systems and training methodology) - Track record of open-source contributions or community building in ML research OUR VIEW ON TALENT Many of the labs that exist today run a credentialist talent model. Concentrate the most already-legible researchers, and assume the concentration compounds. The best researchers in this field are very often not yet legible. Research engineers who have spent years inside a production stack and developed insights no PhD program teaches; PhDs working on the wrong-shaped problem at the right-shaped lab; operators who have been close to real systems long enough to see things credentialed researchers have never had to see. If you don’t have the traditional qualifications and are doing exceptional work, we’d love to chat. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As a Transformative AI Research Economist at Anthropic, you will build macroeconomic models of AI that could be genuinely transformative and develop the scenario-based forecasting tools that let us reason quantitatively about economic trajectories with no historical precedent. You will work on questions of aggregate growth, income distribution, and economic governance under scenarios that most of the profession has not yet modeled seriously. You will ground projections in microeconomic signals from the Anthropic Economic Index — usage patterns across millions of real-world AI interactions, surfaced through privacy-preserving measurement — so that scenario forecasts are disciplined by what we actually observe about task transformation and productivity. You will use frontier methods in growth theory, computational macro, and structural estimation, and contribute to AI-powered tools that expand what economic research can do. Our team combines rigorous empirical methods with novel measurement approaches. We're building first-of-its-kind datasets tracking AI's impact on labor markets, productivity, and economic transformation. Using our privacy-preserving measurement system , we analyze millions of real-world AI interactions to understand how AI augments and automates work across different occupations and tasks. Responsibilities Build macroeconomic models of transformative AI spanning growth, labor markets, and income distribution Develop and maintain scenario-based forecasting tools; publish forecasts for GDP, productivity, and unemployment under a range of AI-capability trajectories Ground macroeconomic projections in microeconomic data from the Anthropic Economic Index, constraining theory with observed patterns of adoption and task transformation Analyze questions of income distribution and economic governance under transformative-AI scenarios Contribute to the development of AI-powered research tools for economics Contribute to Economic Index Reports and publish Research Briefs on first-order questions as they arise Build and maintain relationships with academic institutions, policy think tanks, and other research partners Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders You May Be a Good Fit If You Have PhD in Economics, or an exceptional candidate close to completion Background in macroeconomics, growth theory, or public finance ideally with exposure to task-based frameworks and labor economics A research record that engages seriously with the possibility of transformative AI — you treat the scenarios in this posting as live questions worth modeling rigorously, not speculation to be hedged against Relevant experience in some of: Macroeconomic modeling and structural estimation Scenario-based and time-series forecasting Task-based approaches to technological change Computational methods, agent-based modeling, or large-scale simulation Income distribution and inequality Using large language models in the research workflow Technical skills including: Proficiency in Python, Julia, or similar for computational economics Facility with AI coding agents as part of a research workflow Comfort learning new technical tools and frameworks Demonstrated ability to: Lead research projects from conception to publication Ship on tight timelines and revise in public as new data arrives Communicate technical findings to diverse audiences Strong interest in ensuring AI development benefits humanity Some Examples of Our Recent Work Labor market impacts of AI: A new measure and early evidence Anthropic Economic Index Report: Economic Primitives Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption Estimating AI productivity gains from Claude conversations The Anthropic Economic Index Additional Information For this role, we're looking for candidates who combine rigorous macroeconomic theory with computational fluency, and who are willing to model economic scenarios that fall outside the profession's usual range. The ideal candidate works at the intersection of growth theory, forecasting, and frontier AI. Deadline to apply: None. Applications are reviewed on a rolling basis The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . The Role Turing builds large-scale datasets and reinforcement learning (RL) environments that power post-training for the world’s leading AI labs and enterprises. We create RL environments to evaluate and improve our customers' models on complex, long-range, multi-step workflows across high-GDP-value domains such as Finance, Sales, Retail, Developer Tools, Collaboration, Customer Experience. The environments vary depending on the model capability being evaluated / improved, a few examples of environment types are listed here: Environments for Software Engineering / coding agents UI-Environments for Computer-Use/Browser-Use agents MCP-based Environments for general function-calling agents across various enterprise and consumer applications We are seeking Research Engineers to advance our understanding of frontier AI systems and develop practical innovations in data, algorithms, evaluation, and training. You will work at the intersection of research and engineering, investigating high-impact questions that improve real-world AI systems. You will design and run rigorous experiments, develop research-grade prototypes and tooling, and collaborate with Research, Engineering, Product, and Operations teams to translate promising ideas into scalable applications. This role offers the opportunity to contribute to both foundational research and applied AI development across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. What You’ll Do 1. Conduct Research on Frontier AI Systems Investigate the capabilities, limitations, and training methods of frontier AI systems. Formulate research questions that can inform Turing’s products, platforms, and technical strategy. Explore new approaches to synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarks, and evaluation. Stay current with advances in machine learning and identify opportunities for meaningful technical contribution. 2. Build and Evaluate Research Systems Develop research-grade datasets, experiments, prototypes, tooling, and evaluation frameworks. Train, test, and evaluate models using modern AI and machine learning tools. Analyze results carefully and draw clear, evidence-based conclusions. Establish sound practices for experimental rigor, data quality, reproducibility, and interpretation. Iterate quickly from early hypothesis through validated technical insight. 3. Translate Research into Practical Impact Collaborate closely with Research, Engineering, Product, and Operations teams. Translate research findings into improvements for Turing’s products, platforms, and AI capabilities. Help identify which ideas are ready to move from exploration into scalable, real-world applications. Communicate technical findings clearly to both specialized and cross-functional audiences. 4. Contribute to the Research Community Share findings through technical reports, publications, open-source work, workshops, or conference participation where appropriate. Contribute to Turing’s research culture through technical discussions, peer review, mentorship, and collaboration. Represent Turing thoughtfully within the broader AI research community. What We’re Looking For Research background: PhD or Master’s degree in artificial intelligence, machine learning, computer science, or a closely related technical field; exceptional equivalent research experience will also be considered. Technical depth: Strong foundation in machine learning and practical experience designing experiments, training or evaluating models, and working with modern AI tooling. Relevant expertise: Demonstrated research experience in synthetic or agentic data generation, reinforcement learning or post-training, AI understanding, evaluation, or benchmarks. Research engineering ability: Strong programming skills and the ability to implement, test, and iterate quickly in a research environment. Scientific judgment: Sound judgment around experimental rigor, data quality, reproducibility, and evidence-based decision-making. Communication and collaboration: Clear written and verbal communication skills, intellectual curiosity, and the ability to work effectively across research and engineering teams. A note from our CTO, Ece Kamar Some of the most important questions in AI can't be answered from inside a single lab: how to evaluate frontier models, how data and RL truly drive capability, and what happens when AI meets the real world. At Turing, we work directly with frontier labs, the academic community, and enterprises deploying AI at scale. That creates a feedback loop where deployment shapes our research and our research shapes the field, and we publish our findings, datasets, and benchmarks openly. Join our talented team at Turing to push the frontier of AI with rigorous science that matters. Why Turing Work directly with leading AI labs and enterprises at the frontier of post-training and RL environment design. Build datasets and environments that directly improve the capabilities of advanced AI systems. Help advance coding agents’ ability to understand, plan, and execute complex software-engineering tasks. Apply frontier AI innovations to high-value enterprise workflows. Operate with high autonomy, rapid iteration, and meaningful commercial impact. Collaborate with exceptional colleagues from organizations including Google, Meta, Amazon, and other leading technology companies. Contribute to research that may be shared through technical publications and leading conferences such as ICLR, ICML, and NeurIPS. Compensation: $250,000 to $400,000 OTE + Equity Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .

Location: Remote, Canada Workplace: remote At PointClickCare our mission is simple: to help providers deliver exceptional care. And that starts with our people. As a leading health tech company that’s founder-led and privately held, we empower our employees to push boundaries, innovate, and shape the future of healthcare.   With the largest long-term and post-acute care dataset and a Marketplace of 400+ integrated partners, our platform serves over 30,000 provider organizations, making a real difference in millions of lives. We also reinvest a significant percentage of our revenue back into research and development, ensuring our employees have the resources to innovate and make a lasting impact. Recognized by Forbes as a top private cloud company and honored as one of Canada’s Most Admired Corporate Cultures, we offer flexibility, growth opportunities, and meaningful work.    At PointClickCare, we empower our people to be the architects of a smarter healthcare future; one that is human-first and accelerated by AI to create meaningful and lasting change. Employees harness AI as a catalyst for creativity, productivity, and thoughtful decision-making. By integrating AI tools into our daily workflows, collaboration is enhanced, outcomes are improved, and every team member has the proficiency to maximize their impact. It all starts with our hiring practices where we uncover AI expertise that complements our mission, and we continue to invest in training and development to nurture innovation throughout the employee journey.   Join us in redefining healthcare — so it doesn’t just survive, it thrives. To learn more about PointClickCare, check out Life at PointClickCare and connect with us on Glassdoor and LinkedIn.     **Travel to Office expectations** For Remote Roles: If this role is remote, there will be in-office events that will require travel to and from the Mississauga and/or Salt Lake City office. These will include, but not limited to, onboarding, team events, semi-annual and annual team meetings.   For Hybrid Roles: If this role is Hybrid, there will be an expectation to reside within commutable distance to the office/location specified in the job listing. This will include, but not limited to, weekly/bi-weekly/monthly events in the office with your specific team. This is a requirement for this role. The Role   We're looking for an applied AI researcher who has worked hands-on with healthcare data and is building modern LLM, RAG, and agentic AI systems.   Reporting to the Vice President, Advanced Technology, you will be a member of the Advanced Technology / Applied AI Research team at PointClickCare.   You are a talented Senior Applied Researcher, looking to make a difference solving critical customer and business challenges in the dynamic healthcare market. You thrive applying creative and pragmatic solutions—from framing problems and preparing data, to selecting appropriate techniques, building and evaluating model systems—that will truly transform healthcare and enable our customers to deliver exceptional care.   Responsibilities - Apply machine learning and AI techniques including GenAI and LLM-based approaches to develop model systems and solutions, collaborating across functions to scale and integrate these into large-scale cloud-based SaaS production environments for healthcare. - Work with product leaders, clinical informaticists, data scientists, UI/UX researchers, engineers, and healthcare professionals to design and deliver high value solutions. - Design, build and evaluate solutions that may involve structured or unstructured data for healthcare use cases, delivering capabilities such as predictive models, summarization, recommenders, semantic search, extraction, classification, or other AI/ML applications - Perform research and experimentation to select appropriate approaches, algorithms, and evaluation methods, and execute the R&D needed to deliver production-ready model systems. - Perform data collection, cleaning, analysis, prompt tuning, parameter fine-tuning, model training, development, and evaluation—using existing or developing new tools or workflows as needed. - Apply and help evolve responsible AI evaluation practices, using approaches and frameworks such as LLM-as-judge, RAGAS, and LangSmith, and design and implement custom model performance and quality metrics as needed to ensure model quality, fairness, and safety at scale. - Contribute to a collaborative team culture, sharing knowledge and helping develop new capabilities across the organization.   Required Experience - Ph.D grad, with 1+ years related industry experience OR Masters grad with 4+ years industry experience--including at least 1 year directly related to healthcare ML/AI       - Proven industry experience through multiple major product releases in a commercial SaaS environment. - Hands-on experience working with healthcare data (e.g. EHR, ADT, clinical notes). - Proficiency in Python. Proficiency in Java or other languages helpful. - Proficiency with SQL and data engineering for AI/ML applications. - Experience working with large datasets using big data frameworks (e.g. Azure Data Lake, Apache Spark or Databricks) - Solid understanding of transformer models and LLM-based approaches, including hands-on experience with prompt tuning and PEFT methods (e.g. LoRA, QLoRA) using frameworks such as Hugging Face Transformers. - Experience building and evaluating models using modern ML packages such as NumPy, SciPy, Pandas, Scikit-learn, PyTorch, and LightGBM. - Experience building and deploying models using public cloud infrastructure (Azure, AWS, or Google Cloud), including familiarity with version control, CI/CD pipelines, and scaling considerations for production ML systems at SaaS scale. - Strong communication and collaboration skills; comfortable working on a distributed team. - Experience with one or more of: reinforcement learning or RLHF, NLP techniques for summarization, extraction, classification, or semantic search, retrieval-augmented generation (RAG) pipelines and/or agentic frameworks (e.g. LangChain, LlamaIndex).    #LI-AV1 #LI-remote PointClickCare Benefits & Perks:   Benefits starting from Day 1! Retirement Plan Matching Flexible Paid Time Off Wellness Support Programs and Resources Parental & Caregiver Leaves Fertility & Adoption Support Continuous Development Support Program Employee Assistance Program Allyship and Inclusion Communities Employee Recognition … and more!   It is the policy of PointClickCare to ensure equal employment opportunity without discrimination or harassment on the basis of race, religion, national origin, status, age, sex, sexual orientation, gender identity or expression, marital or domestic/civil partnership status, disability, veteran status, genetic information, or any other basis protected by law. PointClickCare welcomes and encourages applications from people with disabilities. Accommodations are available upon request for candidates taking part in all aspects of the selection process. Please contact recruitment@pointclickcare.com should you require any accommodations. As part of our commitment to a streamlined and equitable hiring experience, PointClickCare uses AI tools to assist with candidate screening and assessment.   When you apply for a position, your information is processed and stored with Lever, in accordance with Lever’s Privacy Policy. We use this information to evaluate your candidacy for the posted position. We also store this information, and may use it in relation to future positions to which you apply, or which we believe may be relevant to you given your background. When we have no ongoing legitimate business need to process your information, we will either delete or anonymize it.  If you have any questions about how PointClickCare uses or processes your information, or if you would like to ask to access, correct, or delete your information, please contact PointClickCare’s human resources team: recruitment@pointclickcare.com    PointClickCare is committed to Information Security. By applying to this position, if hired, you commit to following our information security policies and procedures and making every effort to secure confidential and/or sensitive information. At PointClickCare, base salary is one of the many components that make up our total rewards package. The CAD base salary range for this position is $144,000-181,000 (not overtime eligible) + bonus + benefits. Compensation is assessed individually and aligned to experience, skills, and market context. The posted range reflects typical expectations for this role.