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.
Your time split will vary depending on team needs and your interests, but might look something like this:
Pipeline management, moving things forward: 35%
Coordination and communication with e.g. hiring managers and work test graders: 15%
Process improvements: 10%
Application review: 10%
Conducting and evaluating interviews: 10%
Sourcing and outreach: 10%
Candidate communication and closing: 10%
A few strategic 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?
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.
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.
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
Work trial(s), which might include live conversations and written work
A combined achievements, values, and technical interview with the Manager, Research Recruiting
References
Team structure
This role sits on GiveWell's Research team and reports to our Manager of Research Recruiting. 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. and international locations: $136,000
Benefits:
Our
benefits
include (in the US):
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.
Location: Remote - US
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 USD base salary range for this position is $163,000-204,000 (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.
Location: London, England, United Kingdom
About Wheely
Wheely is redefining premium transportation across major cities in Europe, the US, and the Middle East. We blend cutting-edge technology with the craft of five-star chauffeuring to deliver an experience trusted by more than 100,000 active riders and 1,200 corporate accounts.
We’re a profitable, fast-growing scale-up with $43M raised and over $100M in annual revenue. Having recently launched in New York City, we’re expanding rapidly across the US and EMEA. If you take pride in your craft and want to help shape the next chapter of our growth, we'd love to hear from you.
About The Role
We're hiring Associate Data Scientists with a background in Operations Research. You will own the entire process: from ideation and research through to production deployment.
You'll join either our Pricing or Matching team, working alongside other Data Scientists and engineers on challenging, high-impact problems such as dynamic pricing and bipartite matching.
Responsibilities
Collaborate with engineers, Data Scientists, designers, and product managers to solve ambiguous problems
Research, prototype, and ship ML and optimisation models into production
Continuously improve existing algorithms and identify opportunities for new ones
Requirements
Master's or PhD in Operations Research, Mathematics, Management Science or a closely related field with a focus on optimisation
Strong foundation in mathematical optimisation or mathematical programming
Solid knowledge of probability & statistics
Solid knowledge of Reinforcement Learning and Casual Inference
Excellent programming skills in Python and core ML tooling (e.g. SQL, PyTorch, etc.)
Nice To Have
Exposure to dynamic pricing, auction design, or marketplace matching problems
Publication in Q1/A* journals, participation in NeurIPS and other conferences
What We Offer
Office-based role in West London, four days a week with flexible start and finish times, plus one remote day of your choice
Salary: £50,000 – £70,000
Employee stock options plan
Private medical and dental insurance
Life and critical illness cover
Latest-generation MacBook Pro and 4k display
Monthly credit towards Wheely journeys
Cycle to Work scheme
Professional development stipend
All of your personal information will be collected stored and processed in accordance with
Wheely’s Candidate Privacy Notice
Location: Λευκωσία, Nicosia, Cyprus
About Wheely
Wheely is redefining premium transportation across major cities in Europe, the US, and the Middle East. We blend cutting-edge technology with the craft of five-star chauffeuring to deliver an experience trusted by more than 100,000 active riders and 1,200 corporate accounts.
We’re a profitable, fast-growing scale-up with $43M raised and over $100M in annual revenue. Having recently launched in New York City, we’re expanding rapidly across the US and EMEA. If you take pride in your craft and want to help shape the next chapter of our growth, we'd love to hear from you.
About The Role
We're hiring Associate Data Scientists with a background in Operations Research. You will own the entire process: from ideation and research through to production deployment.
You'll join either our Pricing or Matching team, working alongside other Data Scientists and engineers on challenging, high-impact problems such as dynamic pricing and bipartite matching.
Responsibilities
Collaborate with engineers, Data Scientists, designers, and product managers to solve ambiguous problems
Research, prototype, and ship ML and optimisation models into production
Continuously improve existing algorithms and identify opportunities for new ones
Requirements
Master's or PhD in Operations Research, Mathematics, Management Science or a closely related field with a focus on optimisation
Strong foundation in mathematical optimisation or mathematical programming
Solid knowledge of probability & statistics
Solid knowledge of Reinforcement Learning and Casual Inference
Excellent programming skills in Python and core ML tooling (e.g. SQL, PyTorch, etc.)
Nice To Have
Exposure to dynamic pricing, auction design, or marketplace matching problems
Publication in Q1/A* journals, participation in NeurIPS and other conferences
What We Offer
Office-based role in Nicosia, four days a week with flexible start and finish times, plus one remote day of your choice
Competitive salary
Employee stock options plan
Private medical and dental insurance
Daily Lunch allowance
Latest-generation MacBook Pro and 4k display
Professional development stipend
Relocation support, including visa sponsorship and allowance
All of your personal information will be collected stored and processed in accordance with
Wheely’s Candidate Privacy Notice
Λευκωσία, Nicosia, Cyprus; Relocation support, including visa sponsorship and allowance
Location: Global (Remote)
Workplace: Remote
DECISION RESEARCH SCIENTIST
Team: Research & Development
Location: Flexible / Remote
Reporting to: Head of R&D
ROLE OVERVIEW
Rwazi advances decision systems — software capable of structured reasoning under real-world constraints.
The Decision Research Scientist applies formal reasoning, modeling, and experimentation to real enterprise decision problems.
This role operates at the boundary between research and application.
It translates abstract decision theory, evaluation logic, and structured reasoning into practical system improvements that enhance Rwazi’s decision intelligence.
This is applied decision research — not academic isolation.
CORE MANDATE
The Decision Research Scientist is accountable for:
- Designing formal decision frameworks for complex enterprise problems
- Modeling tradeoffs, uncertainty, and signal ambiguity
- Advancing structured reasoning methodologies
- Testing and validating new decision architectures
- Converting research insight into system-ready primitives
This role strengthens the reasoning depth of Rwazi’s decision engine.
KEY RESPONSIBILITIES
APPLIED DECISION MODELING
- Formalize complex business questions into structured decision systems
- Model uncertainty, tradeoffs, and multi-variable constraints
- Design evaluation logic for ambiguous signal environments
- Develop structured judgment methodologies
RESEARCH-TO-APPLICATION TRANSLATION
- Apply theoretical frameworks to real client use cases
- Test decision logic against live or simulated environments
- Identify structural weaknesses in existing decision pathways
- Propose formal improvements with measurable rigor
EXPERIMENTAL DESIGN
- Design controlled experiments to validate reasoning enhancements
- Define metrics for decision quality and output consistency
- Compare alternative system architectures under defined constraints
CROSS-FUNCTIONAL INTEGRATION
- Collaborate with Research Engineers to prototype research concepts
- Provide formal specifications to Product and Engineering
- Advise leadership on long-term decision capability evolution
ROLE IMPACT
Strong performance in this role results in:
- More reliable and explainable decision outputs
- Increased reasoning depth across signal types
- Expansion into new classes of enterprise problems
- Stronger structural defensibility
This role deepens Rwazi’s intellectual core.
WHAT THIS ROLE IS NOT
- This is not feature delivery
- This is not surface-level analytics
- This is not academic research detached from application
This role requires rigorous thinking applied to real-world constraints.
QUALIFICATIONS AND PROFILE
We are looking for individuals who demonstrate:
- Strong grounding in decision theory, systems modeling, or applied reasoning
- Experience formalizing ambiguous problems into structured frameworks
- Comfort working with AI systems and reasoning architectures
- Ability to design and evaluate experiments rigorously
- Intellectual independence and systems thinking
Candidates may come from applied AI research, quantitative modeling, computational social science, economics, operations research, or advanced analytics domains.
HOW CANDIDATES ARE EVALUATED
Candidates are evaluated based on:
- Their ability to formalize complex decision problems
- The rigor and clarity of their reasoning models
- Evidence of applying theory to practical systems
- Depth of structured thinking
- Their ability to improve decision quality measurably
SUMMARY
The Decision Research Scientist advances Rwazi’s applied decision intelligence by turning complex reasoning into structured, validated system logic.
Location: London
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.
DRW are looking for an experienced
Quant Researcher
to join our global commodities trading team to aid in alpha generation, research, development, and maintenance of analytics infrastructure.
Responsibilities:
Developing systematic trading strategies and alpha generating signals across commodity markets
Enhancing data collection procedures to support improved design, development and maintenance of data pipelines to be used in research/trading
Analyze large, diverse datasets to identify trends as well as building and maintaining models to support trading decisions. Including the use of AI and LLM’s
Extract and organize data from a number of source options, performing quantitative analysis in support of trading
Assist in the development of analytical tools
Translate business needs into data science projects
Collaborate with stakeholders at each stage of project
Communicate projects and results to audiences with different backgrounds
Create and maintain post-trade diagnostics tools across various strategies
Qualifications:
A bachelor’s, master’s, or PhD in mathematics, statistics, physics, engineering, computer science or related field
Experience using AI and LLM’s
Passion and interest for a quant researcher role solving real-world business problems
Minimum of two years' experience in alpha generating role
Experience working with large datasets, data cleaning, preprocessing, and feature engineering
Experience building, validating, deploying, monitoring, and updating predictive models
Excellent programming and scripting skills
Experience working independently and delivering results
Excellent written and oral communication skills, experience communicating complex ideas clearly and concisely to peers and stakeholders
Strong collaborative intent, strong outcome driven focus
Positive, team focused attitude
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
.
#JW1
Location: New York
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.
DRW is looking for Quantitative Researchers to join our expanding Mid-Frequency Systematic Trading team in New York City.
Responsibilities
Apply statistical and machine learning methods across diverse datasets to build trading models that capture novel signals in market behavior.
You will contribute across the full strategy lifecycle — from alpha research and signal generation to portfolio optimization and execution logic.
You will prototype new ideas, investigate strategy components, and write production-quality code to bring them to life.
We're a highly collaborative team that moves quickly and holds a high bar for rigor. We invest in mentorship and hands-on guidance to help you ramp up and grow.
Qualifications
2+ years of prior work experience in statistical arbitrage or systematic trading research
Proven ability to conduct research with large, noisy real-world datasets
Strong programming skills, with proficiency in Python for data analysis and machine learning. Experience with C++ a plus but not required
Degree in a quantitative or technical discipline, such as statistics, computer science, physics, mathematics or economics. All levels welcome, from bachelor's to doctorate
Deep-diving, detail-oriented thinker with a strong bias for action and ability to excel in a fast-paced environment
Ability to own and drive open-ended problems, explore novel approaches, and communicate complex ideas clearly
The annual base salary range for this position is $200,000 to $300,000 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-AC]
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
As a Product Manager for the Research team at Anthropic, you will own the ideation and deployment of new models and products as we advance frontier, safe AI technology. You will work closely with our research teams to help productize credible applied research and identify high-potential use cases grounded in customer needs. We are looking for someone passionate about developing safe and beneficial artificial intelligence technologies. You thrive in ambiguous environments and can uncover latent, non-obvious use cases for nascent technologies, bringing these insights back to research. Your curiosity about user needs enables you to connect advanced technology to products that deliver real-world value.
We seek experienced founders and product managers who can bridge research and ambitious product experimentation, with the ability to create and grow entirely new product categories enabled by AI.
Responsibilities:
Lead vision, strategy, roadmap, and execution of frontier technologies that leverage the latest AI capabilities to solve real-world problems
Partner with research to define, improve, and ship model capabilities within a research area
Identify nascent research that could become transformative products
Synthesize user insights into actionable requirements and evaluations
Understand the AI landscape and ecosystem, ensuring we have an objective view of market capabilities and our position
Work with and influence a diverse set of stakeholders, including research, product engineering, and platform engineering teams
Minimum Qualifications
You might be a good fit if you:
Have a deep passion and curiosity for AI and LLMs. Use AI regularly.
Are a first-principles thinker with the ability to navigate and execute amidst ambiguity, flexing into different domains based on the business problem at hand and finding simple, easy-to-understand solutions
Possess a deep technical background with a strong grasp of AI/ML concepts and are willing to go deep on technical solutions; have a data-driven mindset with working proficiency in Python and SQL
Have strong user empathy and the ability to synthesize vague or contradictory feedback into actionable priorities
Have a track record of launching ambitious products that have achieved distribution or commercial success
Have intellectual curiosity without ego—comfortable asking questions and learning independently
Think creatively about the risks and benefits of new technologies, moving beyond past checklists and playbooks
Have a creative, hacker spirit and love solving puzzles
Preferred Qualifications
Have 5+ years in product management, with experience launching new products and scaling existing ones (founder background is a plus)
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:
$305,000
—
$385,000 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, 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.
Anthropic is at the forefront of AI research, dedicated to developing safe, ethical, and powerful artificial intelligence. Our mission is to ensure that transformative AI systems are aligned with human interests. We are seeking a Research Engineer to join our Pretraining team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems.
Key Responsibilities:
Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
Independently lead small research projects while collaborating with team members on larger initiatives
Design, run, and analyze scientific experiments to advance our understanding of large language models
Optimize and scale our training infrastructure to improve efficiency and reliability
Develop and improve dev tooling to enhance team productivity
Contribute to the entire stack, from low-level optimizations to high-level model design
Qualifications:
Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
Strong software engineering skills with a proven track record of building complex systems
Expertise in Python and experience with deep learning frameworks (PyTorch preferred)
Familiarity with large-scale machine learning, particularly in the context of language models
Ability to balance research goals with practical engineering constraints
Strong problem-solving skills and a results-oriented mindset
Excellent communication skills and ability to work in a collaborative environment
Care about the societal impacts of your work
Preferred Experience:
Work on high-performance, large-scale ML systems
Familiarity with GPUs, Kubernetes, and OS internals
Experience with language modeling using transformer architectures
Knowledge of reinforcement learning techniques
Background in large-scale ETL processes
You'll thrive in this role if you:
Have significant software engineering experience
Are results-oriented with a bias towards flexibility and impact
Willingly take on tasks outside your job description to support the team
Enjoy pair programming and collaborative work
Are eager to learn more about machine learning research
Are enthusiastic to work at an organization that functions as a single, cohesive team pursuing large-scale AI research projects
Are working to align state of the art models with human values and preferences, understand and interpret deep neural networks, or develop new models to support these areas of research
View research and engineering as two sides of the same coin, and seek to understand all aspects of our research program as well as possible, to maximize the impact of your insights
Have ambitious goals for AI safety and general progress in the next few years, and you’re working to create the best outcomes over the long-term.
Sample Projects:
Optimizing the throughput of novel attention mechanisms
Comparing compute efficiency of different Transformer variants
Preparing large-scale datasets for efficient model consumption
Scaling distributed training jobs to thousands of GPUs
Designing fault tolerance strategies for our training infrastructure
Creating interactive visualizations of model internals, such as attention patterns
At Anthropic, we are committed to fostering a diverse and inclusive workplace. We strongly encourage applications from candidates of all backgrounds, including those from underrepresented groups in tech.
If you're excited about pushing the boundaries of AI while prioritizing safety and ethics, we want to hear from you!
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:
£260,000
—
£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: Москва
О нас:
Мы команда Цифрового Ассистента B2C. Мы создаем продукт, который ежедневно помогает десяткам миллионов наших клиентов на новом уровне пользоваться услугами и продуктами Банка и Экосистемы.
Сейчас наша команда разрабатывает новый функционал, позволяющий клиентам решать не только повседневные, но и более сложные комплексные задачи и цели, которые могут состоять из множества взаимосвязанных шагов и быть распределенными во времени.
Для решения задачи в нашем распоряжении цифровая платформа и широкий набор инструментов, представленных мультиагентной системой на основе LLM GigaChat, интегрированной со множеством банковских сервисов.
Мы предлагаем:
· Возможность создавать и развивать инновационный продукт, использующий передовые технологии и задающий тренды в отрасли.
· Работу в кросс-функциональной команде сильных специалистов.
· Широкое пространство для развития и реализации ваших идей и экспертизы.
· Современный технологический стек и развитая IT-инфраструктура.
Обязанности
* Проектирование и разработка новых платформенных инструментов и агентов и расширение функционала существующих
* Проведение глубокого анализа данных и пользовательских сценариев мультиагентной системы, проработка функциональных требований к новым специализированным агентам.
* Разработка и улучшение платформенных механизмов, таких как система роутинга, оркестрации, планирования, управления контекстом и памятью, извлечения инсайтов.
* Разработка комплексной системы метрик качества как отдельных компонентов системы, так целых компонентов системы.
* Создание пайплайнов автоматизации мониторинга и визуализации метрик, в том числе с применением подхода LLM-as-a-Judge.
Требования
· Высшее техническое или математическое образование.
· Опыт в сфере машинного обучения и анализа данных от трех лет.
· Глубокое понимание принципов работы и архитектур LLM, NLP и классических ML-алгоритмов, знание математической статистики и теории вероятностей.
· Владение на высоком уровне основными инструментами и фреймворками для ML разработки, аналитики и обработки больших данных, работы с LLM агентами (pandas, numpy, sklearn, pytorch, langchain, langgraph, pyspark)
· Уверенные практические навыки работы с LLM и промпт-инжениринга.
· Понимание принципов построения промышленных микросервисных архитектур, мультиагентных систем и LLM-приложений.
Условия
· Гибридный режим работы, комфортный современный офис на м. Кутузовская.
· Ежегодный пересмотр зарплаты, годовая премия.
· Корпоративный спортзал и зоны отдыха.
· Более 400 образовательных программ СберУниверситета для профессионального и карьерного развития.
· Расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа.
· Гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ.
· Бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров.
· Вознаграждение за рекомендацию друзей в команду Сбера.
ГибридSan Francisco
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