Вакансии

Responsibilities: — Develop production models for factory planning, pricing, forecasting, inventory, and capacity decisions. Requirements: — Experience with OR-Tools, SQL, Snowflake, PostgreSQL, dbt, Python, Dagster. — Knowledge of forecasting, linear programming, simulation, integer programming, optimisation, constraint programming, and heuristics. Benefits: — Remote work opportunity.

Responsibilities: — Build agentic tools — Develop evaluation systems — Create engineering workflow infrastructure The role involves working with infrastructure, data pipelines, LLMs, failure analysis, multi-agent orchestration, RAG, tool calling, evaluation, MCP, software engineering, DevOps, chip development, fine-tuning, agents, GPU kernels, and context management. Location: San Jose, CA, onsite.

Задачи: — Работа с базами данных — Автоматизация финансовых моделей (Data Analyst) Требования: — Уверенное владение SQL — Продвинутый Excel (таблицы, формулы и др.)

Location: Москва Мы занимаемся pretrain'ом больших языковых моделей в GigaChat: проектируем архитектуру, подбираем рецепт обучения и поддерживаем весь инженерный контур вокруг него. Недавно мы обучили MoE-модель на 700 миллиардов параметров — и на этом не собираемся останавливаться. Обучение идёт на кластерах H100 и B200. GigaChat — самый быстрорастущий проект Сбера, и pretrain — его ядро. **Чем занимается команда** * Архитектура и законы масштабирования. * Рецепт обучения: оптимизаторы, расписание learning rate, нормализация, точность вычислений. * Устойчивость больших прогонов и ускорение сходимости. * Диагностика обучения и оценка изменений с опорой на математический аппарат. * Инженерный контур: воспроизводимость, тесты, CI/CD. * Роль с акцентом на модель, оптимизацию и инфраструктуру обучения, а не на данные. Главная цель — делать обучение быстрее, надёжнее и предсказуемее. **Какие задачи стоят перед командой** * Ускорение цикла «идея → эксперимент → вывод → внедрение». * Снижение количества ручных прогонов и неочевидных сбоев, повышение воспроизводимости и прозрачности результатов. * Повышение надёжности больших прогонов. * Ранняя диагностика деградаций и отделение реальных улучшений от ложных сигналов (расхождение, NaN, коллапс энтропии, артефакты маршрутизации, ложное снижение функции потерь). * Обеспечение безопасного масштабирования при внедрении крупных архитектурных изменений. * Анализ влияния сложных архитектур (например, mixture of experts и маршрутизация) на качество, стабильность и скорость обучения. * Определение и развитие метрик, корректно отражающих изменения в обучении моделей. **Почему мы** Масштаб - 700B MoE уже обучена, дальше — больше. Кластеры на H100 и B200. Публикации. Можно и нужно писать статьи по результатам своей работы — это не ограничивается. Команда - в России нет другой команды, которая занимается pretrain'ом на таком масштабе. Коллеги — люди, которые глубоко разбираются в теме. Влияние. Вы берёте направление целиком. Это не «выполнять задачи из бэклога», а самостоятельно определять, что важно, и доводить до результата. Обязанности * Взять на себя целое направление внутри pretrain'а и развивать его: от постановки задач и планирования экспериментов до внедрения результатов в основное обучение. * Проектировать и проводить эксперименты: формулировать гипотезы, запускать абляции, сравнивать подходы, разбираться в результатах и превращать выводы в решения для основного обучения. * Разбираться с нестабильностью на больших прогонах: искать причины деградаций, строить диагностические метрики, предлагать изменения в оптимизаторе, расписании lr, нормализациях, инициализации, клиппинге, точности вычислений и маршрутизации. * Работать с архитектурой смеси экспертов (MoE): маршрутизатор, балансировка нагрузки, переполнение, артефакты маршрутизации, влияние на качество и производительность. * * Поддерживать большие прогоны и продолжения обучения с чекпоинтов: следить за дрейфом, проверять изменения в коде и конфигурации, снижать риск регрессий. * Улучшать инженерное качество контура обучения: ревью критичных изменений, стратегия тестирования, воспроизводимость экспериментов, профилирование и устранение узких мест. Требования * Глубокое понимание устройства обучения нейросетей: не на уровне обзоров и пересказов, а на уровне, где вы можете объяснить, почему конкретный прогон расходится, глядя на кривые функции потерь, нормы градиентов и энтропии. * Способность самостоятельно взять направление и довести его до результата: от чтения статей и постановки гипотез до внедрения в основной трейн. * Практический опыт с PyTorch и именно с обучением моделей, а не только с инференсом. * Умение доводить исследовательские идеи до надёжного инженерного решения: воспроизводимость, конфиги, тесты, автоматизация, понятные критерии качества. * Хорошую инженерную культуру: аккуратные PR, профилирование, внимание к качеству кода, понятные отчёты об экспериментах. Будет плюсом * Опыт со смешанной точностью и распределённым обучением. * Опыт построения систем оценки моделей или инфраструктуры для экспериментов. Условия * Удалённо. * Возможность оформления в аккредитованную IT-компанию. * Годовая премия по итогам работы до 6 окладов. * Регулярный пересмотр зарплат. * Корпоративный спортзал и зоны отдыха. * Более 400 программ СберУниверситета для роста. * Программа адаптации и помощь руководителя на старте. * Крупнейшее DS&AI community — более 600 DS банка, регулярный обмен знаниями, опытом и лучшими практиками, интерактивные лекции и мастер-классы от ведущих ВУЗов и экспертов технологических компаний, дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира, регулярные внутренние митапы. * Расширенный ДМС, льготное страхование для семьи, корпоративная пенсионная программа. * Ипотека для сотрудников по дисконтной программе. * СберПрайм+ и скидки у партнёров. * Бонус за рекомендации в команду

We're looking for someone who understands the Roblox niche and knows how to turn viral topics into engaging YouTube Shorts scripts. AI DEVELOPER – CLAUDE PIPELINE.

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. The STEM Horizontal team produces the high-signal coding and STEM data frontier labs train on. We're looking for a Research Engineering Manager to lead the research engineering behind its next layer: synthetic data that measurably improves models, and new benchmarks that measure what today's evals miss. You'll lead a team of research engineers, work side by side with research scientists, and stay close enough to the code to review an eval harness line by line or prototype a generator yourself when the team needs it. You'll also build the team : hiring research engineers is a core part of this role, not a side task. Ideally you've been on the other side, at a frontier lab, and know what makes a lab trust a dataset or a benchmark result. Synthetic data that earns its place. Generation, verification, and filtering pipelines where every sample is checked, and where we can show which properties of the data drive downstream gains, not just how much of it we made. Benchmarks that stay meaningful. New evaluations for capabilities current benchmarks miss, built with calibrated difficulty, contamination controls, and statistics strong enough that a lab can make a decision on the result. Quality failures here are quiet. A subtle bug in a verifier doesn't crash anything; it ships thousands of wrong labels into someone's training run and costs weeks. If you want to build the team and the systems that catch that early, this is the seat. What You'll Do 1. Build and Lead the Team Build the team: define the profiles you need, run hiring, and grow research engineers selected for research taste as much as engineering rigor. Run the team's operating rhythm: project selection, planning, experiment reviews, design docs, and clear ownership. Coach engineers on experimental design, code quality, and presenting results; give direct, regular feedback and run fair performance cycles. Define the standards the team works to: eval methodology, data quality bars, and reproducibility — every dataset and result can be regenerated from a commit, a config, and a seed. 2. Partner with Research Scientists Turn hypotheses into experiment plans together: what we're testing, the baseline, the ablations, and the result that would change our minds. Set the agenda with research scientists. You're expected to bring your own hypotheses, not only implement theirs. Take research prototypes to production scale without losing the scientific intent. 3. Build Synthetic Data Systems at Scale Own synthetic data pipelines that generate, verify, filter, deduplicate, and version data, combining programmatic generators, LLM-based generation, automated verifiers, and targeted expert review. Judge data by its effect on models : ablations and fine-tuning or RL experiments that show which attributes (difficulty, diversity, correctness, coverage) drive gains. Build quality systems that hold up at volume: gold sets, LLM-judge calibration against expert labels, sampling audits, and agreement metrics. 4. Create New Benchmarks and Evaluations Find where frontier models fail and where existing benchmarks have saturated, and design evaluations that measure what's missing . Own benchmark methodology end to end: task taxonomy, difficulty calibration, graders and verifiers, contamination controls, held-out splits, and confidence intervals. Ship eval harnesses , including agentic and sandboxed-execution evals, that are deterministic, reproducible, and trusted by internal teams and partner labs. Make the call on whether a result holds up: partner labs and internal teams act on your sign-off. Publish benchmarks, datasets, and findings with our research scientists at venues such as NeurIPS, ICML, and ICLR. 5. Stay Hands-On and Work with Frontier Labs Write code every week on the hardest, highest-leverage parts: core verifier logic, eval statistics, the pipeline component everything else depends on. Expect a third or more of your time on direct technical work. Be the person the team brings its hardest debugging and methodology questions to: why a judge disagrees with experts, why an eval number moved, whether a gain is real or noise. Act as a technical counterpart to researchers at frontier AI labs: scope requests, set acceptance criteria, and explain results and trade-offs plainly. Partner with Strategic Project Leads, delivery, and engineering teams so research advances land in client work and in the platforms the team builds. Sample Projects Identify a capability gap, design and ship a benchmark for it, and see it adopted by partner labs. Stand up a synthetic data pipeline for long-horizon coding tasks with execution-based verifiers, and run the ablations that show which task properties drive RL gains. Calibrate an LLM-judge grading system against expert labels, map where it fails, and decide where humans stay in the loop. Design the experiment that tells a partner lab whether a two-point gain on their eval is real. Decide which of three competing synthetic data approaches the team should bet on, and design the experiments that settle it. Tech Stack Research: Python · PyTorch or JAX · SFT · DPO · PPO / GRPO · RL with verifiable rewards Evals & data: LLM-as-judge · programmatic verifiers · sandboxed execution · gold sets · contamination checks Platform: Python services (Django / FastAPI) · PostgreSQL · Docker · Terraform · AWS or GCP What We're Looking For Experience: 8+ years of hands-on ML or research engineering, at least 5 in industry, including 3+ years directly managing research engineers, ML engineers, or researchers (hiring and performance management included). Synthetic data at scale: you've owned synthetic or curated data pipelines in production whose output drove training decisions , and can show how you measured their quality and downstream impact. Evaluation rigor: you've designed and shipped LLM evals or benchmarks that others used to make model decisions, and understand contamination, saturation, judge bias, inter-annotator agreement, variance, and confidence intervals. Experimental judgment: you plan ablations up front, choose baselines honestly, and can tell a real effect from noise. Technical depth: strong Python; production experience with PyTorch or JAX; working knowledge of the post-training stack (SFT, DPO, PPO/GRPO, RL with verifiable rewards). Technical direction: a track record of setting direction for a team, not only executing a roadmap handed to you. Scale: distributed data processing and large evaluation or training jobs (e.g., Ray, Spark, Kubernetes) on AWS or GCP. Communication: clear written communication for scientists and non-specialists alike; comfortable presenting results to researchers at partner labs. AI-forward: daily use of LLM coding tools (Claude Code, Cursor, Copilot) as part of how you ship, with the judgment to review their output critically. This is a must-have. Education: PhD or Master's degree in AI, machine learning, computer science, statistics, or a closely related field; PhD preferred. Exceptional equivalent research experience will also be considered. We weigh industry outcomes most heavily. Strongly Preferred Frontier AI lab experience: post-training data, evals, or reward modeling at a frontier lab (e.g., OpenAI, Anthropic, Google DeepMind, Meta, xAI) or a top industry research group. Built or contributed to a public benchmark or dataset the community uses. Nice to Have First-author papers at NeurIPS, ICML, ICLR, ACL, EMNLP, or similar. RL environment and verifier design for agentic tasks: coding agents, tool use, computer use, long-horizon workflows. Data valuation or attribution: measuring what each slice of data contributes to a model. Depth in a STEM domain such as math, physics, chemistry, or biology (not required). Experience managing tech leads or managers, or running several concurrent programs. Directing large expert networks through specs, rubrics, and fast feedback loops. 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, evaluation, and synthetic data. Build the datasets and benchmarks that improve and measure the capabilities of advanced AI systems. Shape how frontier labs measure progress on coding and STEM reasoning. 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 $260,000 to $350,000 + Equity Don't meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. 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. 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, 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. https://job-boards.greenhouse.io/anthropic/jobs/4669581008 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 Team Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific frontier. About the role As a Research Engineer on our team you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines. Join us in our mission to develop advanced AI systems pushing the frontiers of science and benefiting humanity. Responsibilities: Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI. Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows Collaborate to translate experimental requirements into production-ready infrastructure Develop large scale data pipelines to handle advanced language model training requirements Optimize large scale training and inference pipelines for stable and efficient reinforcement learning You may be a good fit if you: Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems Are a strong communicator and enjoy working collaboratively Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale Have proven track record of building large-scale data pipelines and distributed storage systems Excel at diagnosing and resolving complex infrastructure challenges in production environments Can work effectively across the full ML stack from data pipelines to performance optimization Have experience collaborating with other researchers to scale experimental ideas Thrive in fast-paced environments and can rapidly iterate from experimentation to production Strong candidates may also have: Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.) Background in building infrastructure for AI research labs or large-scale ML organizations Knowledge of GPU/TPU architectures and  language model inference optimization Experience with cloud platforms (AWS, GCP) at enterprise scale Familiarity with VM and container orchestration. Experience with workflow orchestration tools and experiment management systems History working with large scale reinforcement learning Comfort with large scale data pipelines (Beam, Spark, Dask, …) 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: $350,000 — $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.

• Анализ защищенности и поиск уязвимостей в сетевых устройствах и компонентах. • Анализ двоичного кода с целью поиска ошибок реализации, уязвимостей и/или восстановления логики работы программ (reverse engineering).

Location: United States; Canada; New Zealand; Australia; United Kingdom About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Job We are looking for Life Sciences Researchers with strong, hands-on Python programming skills to contribute to projects focused on evaluating and improving advanced AI systems. In this role, you will combine scientific research expertise with practical software skills to create, solve, and review challenging problems across biology and related disciplines. You will develop reproducible Python-based analyses, process scientific datasets, validate computational results, and identify errors in scientific reasoning, methodology, or code. We particularly welcome Master’s degree holders with strong research and computational experience, as well as PhD holders who have gained practical experience in software engineering, data science, computational research, or other industry data-focused roles. We value candidates who can independently write, understand, debug, and validate code—not simply researchers who have academic knowledge without practical programming ability. LOCATION: US, CA, AUS, NZ, UK (native english speakers); South east, APAC COMMITMENT: Approximately 15 hours per week SCHEDULE: Flexible—you choose your working hours and days, including weekends if preferred. COMPENSATION: Output-based, with payment per task that meets project specifications. Minimum submission requirements apply; the time required per task may vary. Key Responsibilities Create and solve technically rigorous scientific problems within your area of specialization. Develop reproducible Python-based analyses, models, simulations, and data-processing workflows. Analyze and interpret biological or biomedical datasets using appropriate computational methods. Review scientific solutions and Python code for accuracy, clarity, completeness, and reproducibility. Identify errors in scientific reasoning, computational methods, assumptions, and code implementation. Apply relevant statistical and computational techniques to validate research findings. Document methodologies, assumptions, and solutions in a clear and structured manner. Requirements Master’s or PhD in a relevant life sciences discipline, such as Biology, Biomedical Sciences, Bioinformatics, Neuroscience, Genetics, Computational Biology, or a related field. Substantial research experience demonstrated through publications, thesis work, computational projects, industry research, or equivalent accomplishments. Strong practical Python programming skills, particularly for scientific computing, data analysis, modeling, or simulation. Ability to independently write, understand, debug, and validate Python code. Experience working with scientific or biological datasets and interpreting computational results. Strong analytical thinking and the ability to evaluate scientific methods and technical conclusions. Excellent written English and the ability to communicate complex scientific concepts clearly.

Location: Singapore; Malaysia; Philippines; Indonesia; Vietnam; Thailand About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Job We are looking for Life Sciences Researchers with strong, hands-on Python programming skills to contribute to projects focused on evaluating and improving advanced AI systems. In this role, you will combine scientific research expertise with practical software skills to create, solve, and review challenging problems across biology and related disciplines. You will develop reproducible Python-based analyses, process scientific datasets, validate computational results, and identify errors in scientific reasoning, methodology, or code. We particularly welcome Master’s degree holders with strong research and computational experience, as well as PhD holders who have gained practical experience in software engineering, data science, computational research, or other industry data-focused roles. We value candidates who can independently write, understand, debug, and validate code—not simply researchers who have academic knowledge without practical programming ability. LOCATION: US, CA, AUS, NZ, UK (native english speakers); South east, APAC COMMITMENT: Approximately 15 hours per week SCHEDULE: Flexible—you choose your working hours and days, including weekends if preferred. COMPENSATION: Output-based, with payment per task that meets project specifications. Minimum submission requirements apply; the time required per task may vary. Key Responsibilities Create and solve technically rigorous scientific problems within your area of specialization. Develop reproducible Python-based analyses, models, simulations, and data-processing workflows. Analyze and interpret biological or biomedical datasets using appropriate computational methods. Review scientific solutions and Python code for accuracy, clarity, completeness, and reproducibility. Identify errors in scientific reasoning, computational methods, assumptions, and code implementation. Apply relevant statistical and computational techniques to validate research findings. Document methodologies, assumptions, and solutions in a clear and structured manner. Requirements Master’s or PhD in a relevant life sciences discipline, such as Biology, Biomedical Sciences, Bioinformatics, Neuroscience, Genetics, Computational Biology, or a related field. Substantial research experience demonstrated through publications, thesis work, computational projects, industry research, or equivalent accomplishments. Strong practical Python programming skills, particularly for scientific computing, data analysis, modeling, or simulation. Ability to independently write, understand, debug, and validate Python code. Experience working with scientific or biological datasets and interpreting computational results. Strong analytical thinking and the ability to evaluate scientific methods and technical conclusions. Excellent written English and the ability to communicate complex scientific concepts clearly.

Location: India; Japan; South Korea; Taiwan; Hong Kong; Pakistan About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Job We are looking for Life Sciences Researchers with strong, hands-on Python programming skills to contribute to projects focused on evaluating and improving advanced AI systems. In this role, you will combine scientific research expertise with practical software skills to create, solve, and review challenging problems across biology and related disciplines. You will develop reproducible Python-based analyses, process scientific datasets, validate computational results, and identify errors in scientific reasoning, methodology, or code. We particularly welcome Master’s degree holders with strong research and computational experience, as well as PhD holders who have gained practical experience in software engineering, data science, computational research, or other industry data-focused roles. We value candidates who can independently write, understand, debug, and validate code—not simply researchers who have academic knowledge without practical programming ability. LOCATION: US, CA, AUS, NZ, UK (native english speakers); South east, APAC COMMITMENT: Approximately 15 hours per week SCHEDULE: Flexible—you choose your working hours and days, including weekends if preferred. COMPENSATION: Output-based, with payment per task that meets project specifications. Minimum submission requirements apply; the time required per task may vary. Key Responsibilities Create and solve technically rigorous scientific problems within your area of specialization. Develop reproducible Python-based analyses, models, simulations, and data-processing workflows. Analyze and interpret biological or biomedical datasets using appropriate computational methods. Review scientific solutions and Python code for accuracy, clarity, completeness, and reproducibility. Identify errors in scientific reasoning, computational methods, assumptions, and code implementation. Apply relevant statistical and computational techniques to validate research findings. Document methodologies, assumptions, and solutions in a clear and structured manner. Requirements Master’s or PhD in a relevant life sciences discipline, such as Biology, Biomedical Sciences, Bioinformatics, Neuroscience, Genetics, Computational Biology, or a related field. Substantial research experience demonstrated through publications, thesis work, computational projects, industry research, or equivalent accomplishments. Strong practical Python programming skills, particularly for scientific computing, data analysis, modeling, or simulation. Ability to independently write, understand, debug, and validate Python code. Experience working with scientific or biological datasets and interpreting computational results. Strong analytical thinking and the ability to evaluate scientific methods and technical conclusions. Excellent written English and the ability to communicate complex scientific concepts clearly.

Location: San Francisco, CA • New York, NY • United States Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! We’re hiring a Researcher, AI Evaluations to join AI Insights within Figma’s Research team. This is a senior individual contributor role with no direct reports, reporting to the AI Insights lead and working directly with AI Research within AI Platform. You’ll strengthen the team’s model, agent system, and evaluation work through hands-on studies, clear rubrics, and reliable measures of quality that reflect user needs, and your findings will guide model and product improvements. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you’ll do at Figma: Own evaluation research for priority AI Research workstreams. Define quality criteria, design studies, analyze results, and recommend actions Build rubrics, representative evaluation datasets, benchmarks, and quality thresholds that reflect user needs and expert design judgment Work with AI Research and Modeling Platform engineers to integrate validated methods into repeatable evaluations and regression tests Explain quality, failure patterns, and uncertainty. Recommend actions that guide model selection, model improvements, product priorities, and release decisions Validate automated graders against human ratings, including LLM-as-judge methods that use a large language model to score outputs. Identify bias, gaps, and uncertainty Connect user research with AI Research’s model development work. Share reusable methods, review work, and mentor peers We’d love to hear from you if you have: 7+ years of experience in research, applied research, or a related field, with a proven track record of independently leading complex evaluation projects and delivering measurable impact Hands-on experience evaluating AI products, with demonstrated ability to translate evaluation findings into meaningful model and product improvements Expertise in designing human evaluations, scoring rubrics, datasets, and benchmarks, including measuring inter-rater agreement and validating automated graders Strong qualitative and quantitative research skills, with the ability to analyze data, interpret uncertainty, and draw actionable insights into model behavior and performance Excellent problem-solving and stakeholder management skills, with the ability to navigate ambiguity, prioritize risks, balance speed with rigor, and deliver recommendations that influence cross-functional decisions While it’s not required, it’s an added plus if you also have: Experience integrating automated evaluations and regression testing into engineering workflows, with familiarity in tools such as Braintrust, LangSmith, DeepEval, or equivalent platforms Proven ability to develop scalable, reusable evaluation frameworks and drive the adoption of consistent quality standards across teams Background in product design, product management, data science, or engineering, with familiarity in Figma or similar design and developer tools Strong statistical expertise in sampling, significance testing, and agreement metrics, with experience conducting experiments or analyzing performance metrics for AI-powered features Advanced training in a quantitative or behavioral discipline, gained through formal education or equivalent hands-on experience At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you’re excited about this role but your past experience doesn’t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer‑provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law. Annual Base Salary Range: $169,000 — $250,000 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice . Application limits: To help candidates focus on the opportunities most relevant to their skills and experience, Figma limits the number and frequency of applications. These limits apply to all roles: Candidates may submit up to 5 applications during any rolling 30-day period. Candidates may not reapply to the same role while an application is active or within 90 days of receiving a rejection. Once the applicable days has passed, candidates are welcome to apply again.

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. Research Scientist, Discovery (Engineering & Applied Sciences) About the Team Our team is organized around the north star goal of building an AI scientist – a system capable of solving the long term reasoning challenges and basic capabilities necessary to push the scientific and technological frontier. About the role On the Discovery team at Anthropic, we're expanding our work on engineering and the applied sciences. Our goal is to contribute to training Claude to invent, design, and engineer across every discipline and scale, reliably and safely: new manufacturing and experimental techniques, processes, materials, devices, instruments, machines, energy systems, and factories. We want this work to accelerate science and technology and benefit people widely. To achieve these goals, we're seeking exceptional machine learning researchers with deep expertise in training and evaluating frontier models. In this role, you'll work end to end across the model stack, developing training environments, rewards, data, and evaluations that measure and improve Claude's performance on complex engineering and scientific tasks, and carrying what works into production training runs. You'll collaborate closely with researchers, engineers, and partners across Anthropic and industry to build AI systems that can engage in all phases of research and development, and that learn from real-world feedback. A background in engineering or the sciences is a plus but not required. Key responsibilities Design, build, and study reinforcement learning environments, rewards, and training data that improve Claude's capabilities in engineering and the applied sciences Create benchmarks and evaluation frameworks that measure model performance on realistic engineering and scientific workflows Develop simulation-based training environments and verifiers, working with domain experts to validate them against real-world data Develop approaches to long-horizon task completion, multi-agent collaboration, and complex reasoning essential for real engineering and research work Research how models can learn efficiently from experiments and real-world feedback. Study how environments and rewards behave in large-scale training, including robustness, calibration, and generalization, so that training produces models that are both capable and well aligned Scale research ideas from prototype to production training runs Collaborate with domain experts and industry partners to build and test what Claude invents, designs, and engineers, and turn that feedback into training and evaluation signal Help chart how Claude’s engineering capabilities reach real-world use: where they matter most, what stands in the way of adoption, and how that should shape research priorities You may be a good fit if you have: Proficiency in Python and hands-on experience with a modern machine learning framework such as PyTorch or JAX Hands-on experience training or evaluating machine learning models Track record of machine learning research: forming hypotheses, designing and running experiments, and drawing sound conclusions from the results Ability to implement your own research ideas and diagnose problems in complex systems Ability to learn unfamiliar technical fields quickly and work productively with experts in them Clear communication and a collaborative approach to solving problems Excitement about improving Claude’s scientific capabilities and the downstream impact on science and technology Commitment to developing AI that is safe and beneficial Strong candidates may also have: Experience with reinforcement learning and pre-training on large language models, including environment and reward design Familiarity with large-scale language model training, evaluation, and inference pipelines Published research or practical experience in AI for science or engineering, or in long-horizon reasoning A degree or work experience in an engineering or applied science discipline, such as mechanical, electrical, materials, chemical, aerospace, systems, or manufacturing engineering, applied physics, or computational simulation Experience building simulators, surrogate models, or simulation-based environments and verifiers, and calibrating and validating them against real-world data Experience with large-scale data pipelines and technical or multimodal datasets Demonstrated ability to work across multiple domains, such as language modeling, systems engineering, and scientific computing 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: $350,000 — $500,000 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: Москва Мы делаем ML-продукт аналитики деятельности компаний, которые работают с зарубежными партнерами: он прогнозирует их валютные операции и предлагает подходящие продукты банка. Ищем инженера-исследователя, который проведет задачу от гипотезы до прототипа и передаст лучшее решение в разработку. Обязанности * разбираться в задаче вместе с владельцем продукта и аналитиками: что нужно клиенту и какие ограничения у данных * быстро проверять гипотезы на данных, а не на интуиции: от постановки эксперимента до прототипа и демо для заказчика * выбирать подход под задачу: классический ML, временные ряды, LLM, RAG, агенты или их связку. Сравнивать варианты по качеству, устойчивости, стоимости, скорости и сложности вывода в продакшен * проектировать агентов поверх ML-моделей: какие у них инструменты и память, как они собирают контекст и проверяют ответы * передавать лучшее решение в разработку так, чтобы его легко было тиражировать. Требования * опыт внедрения ML-решения с нуля до продакшена * python (pandas, numpy) и SQL, продвинутые навыки построения воспроизводимых пайплайнов * глубокие знания классического ML на табличных данных: бустинг, линейные модели, sklearn, отбор признаков. Навыки работы с PyTorch или аналогом и понимаете, когда DL дает выигрыш * рабата с LLM: промпты, RAG с эмбеддингами и reranking, tool calling, агенты.Знаете типичные сбои вроде галлюцинаций и умеете их ограничивать. оцениваете ответы и агентов целиком через golden set и LLM-as-a-judge, следите за регрессиями качества, latency и стоимостью вызова * умение строить A/B-тесты и офлайн-сравнения: гипотеза, выборки, бейзлайн, абляции, вывод «берем / не берем». Ведете эксперименты в MLflow или аналоге, версионируете данные и промпты * понимаете, когда хватает классического ML, когда нужна LLM * умеете коротко объяснить, что проверили, почему выбрали этот подход и какие риски остались. **Будет плюсом:** * опыт в ВЭД: платежи, валютные потоки, внешнеторговые операции, продукты * сами строили AI-агентов и агентные системы. Условия * комфортный современный офис г. Москва, Кутузовский 32 * офисный формат работы * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * система обучения для профессионального и карьерного развития * расширенный полис ДМС с первого дня работы и страхование для семьи * программа ипотеки для сотрудников * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: United States; Canada; Australia; New Zealand; United Kingdom About Gramian Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. About the Role We are seeking experienced Biology, Bioinformatics, and Computational Biology Specialists to contribute to a specialized AI training and evaluation project focused on advanced biological research and scientific reasoning. We particularly welcome candidates with a Master's degree or PhD in a relevant scientific discipline. PhD holders who have transitioned into industry software engineering, data science, computational research, or other technology-focused roles are especially encouraged to apply. Candidates should have deep expertise in at least one relevant biological specialization and practical experience analyzing real biological datasets. Both research-focused and industry-oriented professionals are welcome. CONTRACT: Contractor LOCATION: Remote – US, Canada, Australia, New Zealand, UK, Southeast Asia, and broader APAC. EDUCATION: Master's or PhD preferred; equivalent relevant experience considered. ENGLISH: C1 minimum. PROJECT: AI training and benchmarking in biology and bioinformatics. AI EXPERIENCE: Not required. Key Responsibilities Design, evaluate, and critique biology, bioinformatics, and computational biology workflows for AI benchmarking initiatives. Provide expert insights into genomic, transcriptomic, proteomic, and other biological data analysis pipelines. Assess and optimize computational pipeline parameters, software configurations, and statistical methods. Analyze biological datasets, including: Next-Generation Sequencing (NGS) data. RNA sequencing and single-cell datasets. Genomic variants and variant calling results. Protein structures and proteomics data. Molecular and cellular biology datasets. Evaluate scientific methodologies and AI-generated biological analyses for accuracy, consistency, and reproducibility. Identify analytical errors, methodological limitations, and opportunities for improving computational workflows. Contribute domain expertise to the development and validation of biology-related AI models and tools. Document findings, methodologies, and best practices to support knowledge transfer. Communicate complex scientific concepts clearly to technical and non-technical collaborators. Requirements Master's or PhD in Biology, Bioinformatics, Computational Biology, Genomics, or a closely related STEM discipline. Equivalent professional industry or research experience may also be considered. Deep expertise in at least one relevant biological or computational specialization. Hands-on experience analyzing real biological datasets. PhD holders with industry experience in software engineering, data science, computational biology, or related technology roles are particularly encouraged. Proficiency in Python and/or R. Experience with relevant scientific tools, depending on specialization. Professional written and spoken English proficiency at C1 level or higher. Strong statistical, quantitative, and analytical reasoning skills.

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 Horizons The Horizons team leads Anthropic's reinforcement learning (RL) research and development, playing a critical role in advancing our AI systems. We've contributed to every Claude release, with significant impact on the autonomy, coding, and reasoning capabilities of Anthropic's models. About the role We're hiring for the Cybersecurity RL team within Horizons. As a Research Engineer, you'll help to safely advance the capabilities of our models in secure coding, vulnerability remediation, and other areas of defensive cybersecurity. This role blends research and engineering, requiring you to both develop novel approaches and realize them in code. Your work will include designing and implementing RL environments, conducting experiments and evaluations, delivering your work into production training runs, and collaborating with other researchers, engineers, and cybersecurity specialists across and outside Anthropic. The role requires domain expertise in cybersecurity paired with interest or experience in training safe AI models. For example, you might be a white hat hacker who's curious about how LLMs could augment or transform your work, a security engineer interested in how AI could help harden systems at scale, or a detection and response professional wondering how models could enhance defensive workflows. You may be a good fit if you: Have experience in cybersecurity research. Have experience with machine learning. Have strong software engineering skills. Can balance research exploration with engineering implementation. Are passionate about AI's potential and committed to developing safe and beneficial systems. Strong candidates may also have: Professional experience in security engineering, fuzzing, detection and response, or other applied defensive work. Experience participating in or building CTF competitions and cyber ranges. Academic research experience in cybersecurity. Familiarity with RL techniques and environments. Familiarity with LLM training methodologies. 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.

Основная задача специалиста — создавать сложные, но реалистичные и проверяемые security-сценарии, в которых LLM-модели совершают ошибки именно из-за недостатков в рассуждении (reasoning) , а не из-за неоднозначных инструкций или ограничений среды.

- Build evaluation frameworks for AI agents, including datasets, sandbox environments and metrics for real-world tasks. - Hands-on experience building, evaluating or improving LLM models or agents (professional, research, open-source or personal projects).