machine learning engineer

ai engineer

data engineer

senior software engineer

full stack engineer

research engineer

Resolution

<div><h1 class="jobSectionHeader">About Resolution</h1>
Resolution does research on how to align artificial superintelligence (ASI). ASI may be developed in the next few years, but it is unclear whether alignment is on track to be ready in the same timeframe. We aim at higher a priori confidence in aligned outcomes by pursuing a portfolio of theory and empirics bets, any one of which &mdash; if it succeeds &mdash; would meaningfully advance the field. We invest heavily in research automation to accelerate progress, and we believe that stronger alignment theory unlocks higher automation: more principled approaches give us better filters for which directions of automated research are promising.
Resolution was founded in 2026 by researchers from UK AISI's Alignment Team, who ran the &pound;30m Alignment Project, and Timaeus, who pioneered applying singular learning theory to alignment.
For more information, see our announcement.<h1 class="jobSectionHeader">
About the Team</h1>
We are hiring Research Engineers across several main focus areas. We expect the boundaries between these areas to be flexible, but please indicate which mode you're more interested in (or &quot;either&quot;) in your application.
Research Automation (primary focus). A cross-cutting function that builds the infrastructure and tooling our researchers use to scale their work, increasingly leveraging fleets of AI research assistants alongside small teams of humans.
Program-embedded Research Engineering (also hiring). Research engineers embedded within one of our research programs (scalable oversight, complexity theory, learning theory, personas, and possible future programs like heuristic arguments or game theory), partnering with researchers on scaling experiments, building program-specific infrastructure, and translating theoretical insights into empirical tools.<h2 class="jobSectionHeader">
About the Role</h2>
Research Engineers at Resolution are core members of our research teams, directly driving both research and the core infrastructure behind it. We believe clean engineering on automation, experimentation, and infra is essential to ambitious research, and that excellence on this front requires active research participation.<h2 class="jobSectionHeader">
Responsibilities</h2><ul><li>
(Research automation track) Build agentic research infrastructure: experiment orchestration, hypothesis generation, automated analysis pipelines; autoformalization tooling for the theory side; internal AI-powered tools for researchers</li><li>
(Program-embedded track) Scale program experiments to frontier-tier models; build program-specific infrastructure; partner with researchers on engineering and implementation.</li><li>
(Both) Maintain and extend distributed training, experiment, and evaluation infrastructure</li><li>
(Both) Contribute to and maintain shared codebases across the org</li><li>
(Both) Communication of engineering &amp; automation progress, obstacles &amp; learnings to your team and the wider org via Slack and in weekly meetings.</li></ul>
We're on the lookout for excellence, so if you're a cracked engineer who doesn't precisely fit these descriptions, please still apply!<h2 class="jobSectionHeader">
You May Be a Good Fit If You</h2><ul><li>
Have a strong software engineering background, including production-quality Python</li><li>
Have deep experience with ML frameworks (PyTorch or Jax) and distributed-training stacks</li><li>
Have a demonstrated ability to ship complex systems end-to-end</li><li>
Have a Bachelor's degree or equivalent in CS, physics, math, ML, or related</li><li>
Are willing to use AI tools aggressively in your own workflow, with appropriate care to not get fooled!</li><li>
Are motivated by alignment of artificial superintelligence (ASI) and want to contribute to it full-time.</li></ul><h2 class="jobSectionHeader">
Strong Candidates May Also Have</h2><ul><li>
Experience with autoformalization, Lean, or other proof-assistant tooling</li><li>
Background in research infrastructure or ML platform engineering at frontier labs</li><li>
Experience scaling ML systems to 100B+ parameter scale</li><li>
Experience with CUDA kernel development or GPU optimization</li><li>
Familiarity with alignment research</li></ul><h1 class="jobSectionHeader">
Application Process</h1>
Initial screener. A 20-minute conversation with a senior researcher to discuss your research background, motivations, and interests.
Work trial. A 4-hour take-home assignment. We'll collect references in parallel. We'll compensate you for your time, subject to right-to-work constraints.
Work trial review. A 45-minute discussion of your work trial.
Research talk. A 30&ndash;40 minute interactive conversation about your previous research.
Senior 1:1. A 30-minute conversation with a member of our research leadership.<h1 class="jobSectionHeader">
Logistics</h1>
Salary: Your salary depends on the scope, autonomy, and impact we expect you to have while working at Resolution. The expected range of salaries for this role is:<ul><li>
L3 (SWE I): $141,000 remote; $236,000 in-person</li><li>
L4 (SWE II): $208,000 remote; $346,000 in-person</li><li>
L5 (Senior SWE): $270,000 remote; $451,000 in-person</li><li>
L6 (Staff SWE): $402,000 remote; $670,000 in-person</li><li>
L7 (Senior Staff SWE): $738,000 remote; $930,000 in-person</li></ul>
Strong early-career engineers will typically come in at L3. Someone with significant independent research engineering experience would likely start at L4 or L5.
Location: Berkeley, California. Remote may be considered in exceptional cases.
Benefits:<ul><li>
5 weeks of paid vacation per year, in addition to public holidays.</li><li>
Comprehensive healthcare insurance (medical, dental, vision).</li><li>
Unlimited sick leave to prioritize your well-being.</li><li>
An unconditional 401(k) contribution equal to 4% of your salary.</li></ul>
Visa sponsorship: We can sponsor visas for relocation to Berkeley.
Minimum education: A bachelor's degree in a field relevant to the role, or an equivalent combination of education, training, and/or professional experience that demonstrates comparable knowledge.
Deadline: Applications are rolling &mdash; please apply ASAP. We'll respond to applications within one week of receipt.
Start date: ASAP.
Compensation Range: $141K - $930K</div>

Research Engineer

DeepMind

<div><h3 class="jobSectionHeader">Minimum qualifications:</h3><ul>
<li>Bachelor's degree in Computer Science, Physics, Mathematics, Chemistry, Engineering, a related field, or equivalent practical experience.</li>
<li>5 years of experience in machine learning, engineering, and software development using Python.</li>
<li>Experience with data structures or algorithms in an academic or industry setting</li></ul> 
<h3 class="jobSectionHeader">
Preferred qualifications:</h3><ul>
<li>PhD in a relevant scientific or computational field.</li>
<li>Experience in agentic AI and modern machine learning paradigms (e.g., diffusion, transformers, agentic tools, LLMs).</li>
<li>Knowledge of quantum computing concepts.</li>
</ul><div><h2 class="jobSectionHeader">About the job</h2>
At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

As a Research Engineer on our Science Team, you will contribute to our Quantum Computing Initiative. This role is an excellent opportunity for a creative and driven engineer who truly burns for the immense possibilities of AI for Science. 
 
In this role, you will act as the crucial bridge between research and implementation, by seamlessly linking quantum computing research with ML. As an amazing team player, you will collaborate with domain experts in (quantum) computer science, mathematics, computational chemistry, physics, and machine learning.
Artificial intelligence will be one of humanity&rsquo;s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.<div>
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.</div></div><div><h2 class="jobSectionHeader">
Responsibilities</h2><ul>
<li>Deliver on complex research and engineering milestones, assuming end-to-end ownership of implementing and scaling ML solutions (traditional and agentic) at the intersection of quantum computing and machine learning.</li>
<li>Complement the algorithms development pillar by translating classical and quantum algorithms into robust code, balancing implementation time with efficiency, design simplicity, and sustainability.</li>
<li>Facilitate communication between research and engineering by advocating code readability, promoting engineering best practices, and writing effective design documents.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Research Engineer, Quantum Computing, DeepMind

Anthropic

<div><div><h2 class="jobSectionHeader">About Anthropic</h2>

Anthropic&rsquo;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.</div><div><h2 class="jobSectionHeader">
About the role</h2>

Anthropic's RL Scaling Science team studies how reinforcement learning behaves as we scale it (across model size, compute, and task horizon) and turns that understanding into the training recipes behind our frontier models. As a Research Engineer on this team, you'll design and run large-scale experiments to understand and resolve bottlenecks, build the benchmarks that make long-horizon progress measurable, and ship validated findings directly into production training.

This role lives at the boundary between research and engineering. The problems are open, the experiments run at frontier scale, and the path from a robust result to production is short.
<h2 class="jobSectionHeader">
Key responsibilities</h2>
<ul><li>Design, run, and interpret large-scale RL experiments, reasoning rigorously about what the data does and doesn't show</li>
<li>Investigate how RL improves as horizon, compute, and model size grow</li>
<li>Build and maintain benchmarks for long-horizon RL so progress is measurable and reproducible</li>
<li>Translate validated findings into production training recipes, exercising judgment about when a result is robust enough to ship</li>
<li>Debug complex issues at the seam where research meets infrastructure - failures that only appear at scale</li>
<li>Partner closely with adjacent RL teams across research and engineering and advance our overall RL stack</li>
</ul><h2 class="jobSectionHeader">Minimum qualifications</h2>
<ul><li>Strong empirical research skills in Reinforcement Learning, large-scale ML training, or a closely adjacent area</li>
<li>Demonstrated ability to own large experiments end-to-end, from design through interpretation</li>
<li>Proficiency in Python and experience working with large-scale or distributed ML systems</li>
<li>Comfort operating at the research/systems boundary, including debugging where the two meet</li>
<li>Care about the societal impacts of AI and responsible scaling</li>
</ul><h2 class="jobSectionHeader">Preferred qualifications</h2>
<ul><li>Published or shipped work in long-horizon RL or RL fundamentals</li>
<li>Experience translating research findings into production training recipes</li>
<li>Demonstrated large scale industry impact via RL interventions</li>
<li>Experience working on frontier-scale training runs with long trajectories</li>
</ul><h2 class="jobSectionHeader">Representative projects</h2>
<ul><li>Design a benchmark suite for long-horizon RL that distinguishes genuine capability gains from artifacts of evaluation setup</li>
<li>Take a promising experimental finding, stress-test it across model scales, and work with training teams to land it in a production recipe</li>
<li>Investigate an unexpected scaling trend in an RL run and trace it to a root cause spanning algorithm, data, and infrastructure</li></ul></div><div><div><div><div> 
The annual compensation range for this role is listed below.

For sales roles, the range provided is the role&rsquo;s On Target Earnings (&quot;OTE&quot;) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.</div></div>
Annual Salary:
&pound;375,000 - &pound;640,000 GBP</div></div><div><h2 class="jobSectionHeader">
Logistics</h2>

Minimum education: Bachelor&rsquo;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.

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&mdash;visit anthropic.com/careers directly for confirmed position openings.
<h2 class="jobSectionHeader">
How we're different</h2>

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 &mdash; advancing our long-term goals of steerable, trustworthy AI &mdash; 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 &amp; Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
<h2 class="jobSectionHeader">
Come work with us!</h2>

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.</div></div>

Research Engineer, RL Scaling Science

Thomson Reuters

<div>
 Join a cutting-edge research team working to deliver on the transformation promises of modern AI. We are seeking Machine Learning Research Engineers with the skills and drive to build and conduct experiments with advanced AI systems in an academic environment rich with high-quality data from real-world problems.
 
 
 Foundational Research is the dedicated core Machine Learning research division of Thomson Reuters. We are focused on research and development, with a particular focus on advanced algorithms and training techniques for Large Language Models (LLMs). We are expanding our strong foundation of research capabilities across different areas and are looking for engineers who participate in designing, coding, conducting experiments, and translating findings into concrete deliverables.
 
 
 
 Our Focus Areas Are
 
 
 
 <ul>
 <li>
 LLM Training (Continued pretraining, instruction tuning, reinforcement learning, distributed training, efficient ML techniques)
 </li>
 <li>
 Post-training techniques for planning, reasoning &amp; complex workflows (e.g., reasoning models, LLMs + knowledge graphs, test time compute, CoT pipelines, tool use &amp; API calling, etc.)
 </li>
 <li>
 Data-centric Machine Learning (Synthetic data, curriculum learning, learned data mixtures, etc.)
 </li>
 <li>
 Evaluation (Benchmarking best practices, humans/LLMs as a judge, red teaming/adversarial testing, hallucination detection, etc.)
 
 
 </li>
 </ul>
 We work collaboratively with TR Labs (TR's applied research division), academic partners at world-leading research institutions, and subject matter experts with decades of experience. We experiment, prototype, test, and deliver ideas in the pursuit of smarter and more valuable models trained on an unprecedented wealth of data and powered by state-of-the-art technical infrastructure. Through our unique institutional experience, we have access to an unprecedented number of subject matter experts involved in data collection, testing and evaluation of trained models.
 
 
 As an ML Research Engineer, you will play a key part in a diverse global team of experts. We hire world-leading specialists in ML/NLP/GenAI, as well as Engineering, to drive the company's leading internal AI model development. You will have the opportunity to contribute to our proprietary AI model research &amp; development through rapid prototyping, scalable infrastructure, and production-quality implementations, and to research papers in top tier academic conferences and journals.
 
 
 
 About The Role
 
 
 
 In this opportunity, as an ML Research Engineer you will:
 
 
 <ul>
 <li>
 Build: You will design and implement robust, scalable systems for training and evaluating large language models. You'll build data pipelines for data-centric research, training infrastructure for instruction fine-tuning (IFT), Direct Preference Optimization (DPO), and reinforcement learning workflows, evaluation frameworks for comprehensive model assessment, and infrastructure for agentic workflows that enables researchers to iterate quickly and effectively.
 </li>
 <li>
 Innovate: You will work at the very cutting edge of AI Research at an institution with some of the richest data sources in the world. Through your work, you will rapidly implement novel research ideas in LLM training, evaluation, agentic systems, and data processing, transforming them into production-ready systems and research publications. You will contribute to advancing the state-of-the-art in data-centric ML by building tools that help us make the best use of our unprecedented data resources.
 </li>
 <li>
 Experiment and Develop: You are involved in the entire research &amp; model development lifecycle, brainstorming, coding, testing, and delivering high-quality implementations that support cutting-edge research. You'll build pipelines for synthetic data generation, automated evaluation systems, training workflows for various fine-tuning approaches, and agent-based workflows that push the boundaries of what's possible with LLMs.
 </li>
 <li>
 Collaborate: Working on a collaborative global team of research engineers and scientists both within Thomson Reuters and our academic partners at world-leading universities. You'll work closely with researchers to understand their needs and translate cutting-edge research papers into practical, scalable implementations.
 </li>
 <li>
 Communicate: Actively engage in sharing technical implementations and best practices with the wider team through code reviews, documentation, technical presentations, and knowledge sharing sessions. Contribute to internal research discussions and stay current with the latest developments in LLM training, evaluation, agentic AI, and data-centric machine learning.
 
 
 </li>
 </ul>
 
 About You
 
 
 
 You're a fit for the role if your background includes:
 
 
 
 Required Qualifications
 
 
 
 <ul>
 <li>
 Bachelor's or Master's degree in Computer Science, Engineering, or a relevant discipline (or equivalent practical experience)
 </li>
 <li>
 3+ years of hands-on experience building ML/NLP/AI systems with strong software engineering practices
 </li>
 <li>
 Demonstrated expertise in building production-quality code and data pipelines for ML systems
 </li>
 <li>
 Proficiency in modern AI development frameworks including: PyTorch, Jax , HuggingFace Transformers, LLM APIs (litellm etc) and vLLM for building and deploying large-scale AI applications
 </li>
 <li>
 Understanding of LLM training methodologies including instruction fine-tuning, preference optimization, and reinforcement learning approaches
 </li>
 <li>
 Strong software engineering skills including version control, testing, CI/CD, and code quality practices
 </li>
 <li>
 Hands-on experience with experiment tracking and orchestration tools such as clearml, Weights &amp; Biases, MLflow.
 </li>
 <li>
 Experience with distributed computing frameworks and large-scale data processing (e.g., Ray, Spark, Dask)
 </li>
 <li>
 Excellent communication skills to collaborate with researchers and translate research ideas into robust implementations
 </li>
 <li>
 Self-driven attitude with genuine curiosity about ML research developments
 </li>
 <li>
 Comfortable working in fast-paced, agile environments, managing the uncertainty and ambiguity of genuinely novel research
 
 
 </li>
 </ul>
 
 Helpful Qualifications
 
 
 
 <ul>
 <li>
 Track record of ML impact in the form of releases, publications or contributions to open source ML libraries or frameworks (especially in training, evaluation, data processing, or agent systems)
 </li>
 <li>
 Experience building and maintaining ML training infrastructure and data pipelines at scale
 </li>
 <li>
 Extensive experience with LLM training techniques such as instruction fine-tuning (IFT), Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), or other RLHF methods
 </li>
 <li>
 Hands-on experience implementing and scaling supervised fine-tuning, preference learning, and reinforcement learning pipelines for LLMs
 </li>
 <li>
 Experience building LLM evaluation frameworks, benchmarking systems, or automated testing pipelines
 </li>
 <li>
 Hands-on experience with agentic workflows, tool-using AI systems, or multi-agent coordination (examples include: langgraph, AutoGPT, LLamaIndex)
 </li>
 <li>
 Experience with data-centric ML approaches including synthetic data generation, data curation, or curriculum learning pipelines
 </li>
 <li>
 Experience training large-scale models over distributed nodes with cloud tools such as AWS, MS Azure, or Google Cloud
 </li>
 <li>
 Hands-on experience with MLOps, experiment tracking, and model deployment systems
 </li>
 <li>
 Strong interest in staying current with ML research literature and ability to quickly implement novel techniques from academic papers
 </li>
 <li>
 Familiarity with training optimization techniques such as mixed precision training, gradient checkpointing, and efficient attention mechanisms
 </li>
 <li>
 Knowledge of modern ML engineering practices (containerization, orchestration, monitoring)
 
 
 </li>
 </ul>
 
 You Will Enjoy
 
 
 
 <ul>
 <li>
 Learning and development: On-the-job coaching and learning as well as the opportunity to work with cutting-edge methods and technologies.
 </li>
 <li>
 Plenty of data, compute, and high-impact problems: Our scientists and engineers get to explore large datasets and discover new capabilities and insights. Thomson Reuters is best known for the globally respected Reuters News agency, but our company is also the leading source of information for legal, corporate, and tax &amp; accounting professionals. We have over 60,000 TBs worth of legal, regulatory, news, and tax data. We also provide access to significant cloud computing resources for our researchers and engineers.
 </li>
 <li>
 Competitive compensation &amp; benefits packages
 
 
 </li>
 </ul>
 
 What&rsquo;s in it For You?
 
 
 
 <ul>
 <li>
 Hybrid Work Model: We&rsquo;ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected.
 </li>
 <li>
 Flexibility &amp; Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
 </li>
 <li>
 Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow&rsquo;s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future.
 </li>
 <li>
 Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
 </li>
 <li>
 Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
 </li>
 <li>
 Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.
 </li>
 <li>
 Making a Real-World Impact: We are one of the few companies globally that helps its customers pursue justice, truth, and transparency. Together, with the professionals and institutions we serve, we help uphold the rule of law, turn the wheels of commerce, catch bad actors, report the facts, and provide trusted, unbiased information to people all over the world.
 
 
 </li>
 </ul>
 
 About Us
 
 
 
 Thomson Reuters informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news.
 
 
 We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward.
 
 
 As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug-free workplace.
 
 
 We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law. More information on requesting an accommodation here.
 
 
 Learn more on how to protect yourself from fraudulent job postings here.
 
 
 More information about Thomson Reuters can be found on thomsonreuters.com.
 
 
</div>

Machine Learning Research Engineer (Foundational Research)

Polymodal

<div>
 
 
 Every role at Polymodal is dual: founding platform builder first, with a ML research and signal intelligence edge that shapes the platform.
 
 
 
 
 
 
 As agents take on more execution, the platform needs models that understand behavior, predict action, and improve from its own data. You'll build the signal intelligence that makes the platform adaptive.
 
 
 
 
 
 Stack: Python/Rust, classifiers, eval pipelines, tiny predictive models, local / edge inference, inference routing, and world models for action prediction.
 
 
 What you'll own
 <ul>
 <li>
 Turn behavioral data into fast, tiny, useful models.
 </li>
 <li>
 Build and ship ML classifiers over agent and host telemetry.
 </li>
 <li>
 Build training/eval pipelines on our own data.
 </li>
 <li>
 Package models as production services (API, batch, edge).
 </li>
 <li>
 Optimize inference cost and latency: quantization, distillation, batching, caching.
 </li>
 <li>
 Partner with data engineering to turn raw signal into model-ready features.
 
 </li>
 </ul>
 About you
 <ul>
 <li>
 You care about models that survive production, not just notebooks or benchmarks.
 </li>
 <li>
 You turn messy behavioral data into hypotheses, evals, and shipped systems.
 </li>
 <li>
 You move fast without losing scientific honesty.
 </li>
 <li>
 You work well where research, product, and systems blur.
 
 </li>
 </ul>
 Must-haves
 <ul>
 <li>
 You use AI as part of your own research and engineering loop, not as a side tool.
 </li>
 <li>
 You have shipped ML into production and owned it end to end.
 </li>
 <li>
 Strong in Python/Rust and a modern ML framework (PyTorch or TensorFlow).
 </li>
 <li>
 Background in anomaly detection, sequence, or behavioral modeling.
 </li>
 <li>
 Experience with edge, embedded, or low-latency inference.
 
 </li>
 </ul>
 Nice-to-haves
 <ul>
 <li>
 Experience with small-model training, distillation, or quantization.
 </li>
 <li>
 Publications, notable GitHub work, or hard side projects.
 
 </li>
 </ul>
 About Polymodal
 
 Polymodal is a funded pre-seed company building the operating layer for agent-executed organizations. We closed our pre-seed in May 2026, backed by Lunar Ventures, Wind Capital, Volve Capital, and early-stage angels. We are early enough for every hire to shape the company, and real enough that the work must survive customers, scale, security, and trust.
 
 
 Co-founders &amp; team
 
 You'll build directly with Joep (3x founder, cybersecurity), Sean (former CISO at Slack/Reddit/Bumble), Edo (2x founder in AR/XR and multi-agent systems), and Atul (founder, cybersecurity). We are future-facing, execution-focused, daring, and human.
 
 
 Our offer
 <ul>
 <li>
 Competitive salary and meaningful early equity, so you share real ownership.
 </li>
 <li>
 A chance to define a new category while product, architecture, and company are still malleable.
 </li>
 </ul>
 
 
 
</div>

Machine Learning Research Engineer

Reflection

<div>
 
 Our Mission
 
 
 
 Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all.
 
 
 
 The Roles Mission
 
 
 
 Bridge the gap between research and production by turning cutting-edge algorithms into scalable training systems. You will design and optimize the core infrastructure behind frontier AI models &mdash; from reinforcement learning training loops and distributed GPU training to massive-scale data pipelines.
 
 
 Our systems train models across thousands of GPUs and process petabyte-scale datasets. We care deeply about numerical stability, throughput, and reproducibility.
 
 
 
 What This Team Does
 
 
 
 This team owns and evolves the core infrastructure behind our training systems.
 
 
 
 We Focus On
 
 
 
 <ul>
 <li>
 Reinforcement learning training infrastructure
 </li>
 <li>
 Distributed training and inference systems
 </li>
 <li>
 Experiment infrastructure and reproducibility
 </li>
 <li>
 Large-scale data pipelines
 
 
 </li>
 </ul>
 The goal is to build the engineering foundation that allows researchers to iterate quickly while training models at massive scale.
 
 
 
 About The Role
 
 
 
 You will architect and optimize the core training infrastructure that powers our models. This includes RL training loops, distributed GPU systems, and large-scale data pipelines.
 
 
 You will work closely with researchers to transform new ideas into reliable, scalable training systems.
 
 
 
 Responsibilities Include
 
 
 
 <ul>
 <li>
 Designing and optimizing large-scale training loops and data pipelines.
 </li>
 <li>
 Implementing state-of-the-art techniques and ensuring they are numerically stable and computationally efficient.
 </li>
 <li>
 Building internal tooling for launching, monitoring, and reproducing complex experiments.
 </li>
 <li>
 Diagnosing deep bottlenecks across the training stack (GPU memory issues, communication overhead, dataloader stalls).
 </li>
 <li>
 Translating research prototypes into reusable, production-grade infrastructure.
 
 
 </li>
 </ul>
 
 What You'll Work With
 
 
 
 
 Distributed Training
 
 
 
 <ul>
 <li>
 GPU parallelism (data, tensor, pipeline, expert)
 </li>
 <li>
 Large-scale distributed training infrastructure
 </li>
 <li>
 Communication optimization (NCCL, RDMA, GPU interconnects)
 </li>
 <li>
 FSDP / ZeRO and model sharding
 
 
 </li>
 </ul>
 Orchestration &amp; Runtime Systems
 
 
 <ul>
 <li>
 Ray, Kubernetes, Slurm
 </li>
 <li>
 Distributed runtimes and async systems
 </li>
 <li>
 Containerization and sandboxing
 
 
 </li>
 </ul>
 Frameworks
 
 
 <ul>
 <li>
 PyTorch
 </li>
 <li>
 JAX
 </li>
 <li>
 Megatron-style training stacks
 </li>
 <li>
 Triton / custom kernels
 
 
 </li>
 </ul>
 Data Infrastructure
 
 
 <ul>
 <li>
 Large-scale dataset curation pipelines
 </li>
 <li>
 Deduplication and filtering systems
 </li>
 <li>
 Tokenization and preprocessing
 </li>
 <li>
 Distributed data processing frameworks
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <ul>
 <li>
 You are a strong software engineer who speaks the language of machine learning.
 </li>
 <li>
 You may not have a PhD, but you know how to implement a research paper.
 </li>
 <li>
 You have deep experience in at least one of the following: Distributed Training &amp; Inference or Data Infrastructure
 </li>
 <li>
 You enjoy working at the boundary between:
 </li>
 <ul>
 <li>
 Machine learning algorithms
 </li>
 <li>
 Distributed systems
 </li>
 <li>
 High-performance computing
 </li>
 </ul>
 <li>
 You care deeply about performance, numerical stability, and reproducibility.
 </li>
 <li>
 You thrive in high-agency environments and enjoy solving hard technical problems.
 
 </li>
 </ul>
 
 What We Offer
 
 
 
 We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models.
 
 
 We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported.
 
 
 <ul>
 <li>
 Top-tier compensation: Salary and equity structured to recognize and retain our talent globally.
 </li>
 <li>
 Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options.
 </li>
 <li>
 Health &amp; wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance.
 </li>
 <li>
 Meals: Lunch and dinner are provided in the office daily.
 </li>
 <li>
 Life &amp; family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys.
 </li>
 <li>
 Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K.
 </li>
 <li>
 Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable.
 </li>
 <li>
 Team building: We have regular off-sites, happy hours, and team celebrations.
 
 
 </li>
 </ul>
</div>

Member of Technical Staff - Research Software Engineer

JPMorganChase

<div>JOB DESCRIPTION 
 
 
Be an integral part of a technology team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.
As a Python Quant Data Engineer you will help build the technology for Systematic Equities Trading Business. The role would sit in the Equities Trading Data &amp; Analytics technology team. As a Vice President Software Engineer at JPMorgan Chase within the agile technology team, you will play a crucial role in improving, developing, and delivering top-tier technology products in a secure, stable, and scalable manner. Your skills and contributions will have a substantial impact on the business, and your profound technical expertise and problem-solving methodologies will be utilized to address a wide range of challenges across various technologies and applications.
Job Responsibilities<ul><li>
Build and support fast, reliable, globally consistent data pipelines (data ingestion, cleaning, backfilling, storing) for the research and execution systems ensuring data integrity and low-latency access for research and trading.</li><li>
Work with the research and trading teams to onboard new datasets efficiently and consistently for use globally by the business.</li><li>
Design and build robust tools and frameworks to support quantitative research and production trading.</li><li>
Design, build and support research infrastructure (e.g. data access APIs, high performant and scalable simulation environments, feature and strategy signal stores)</li><li>
Build and support research and trading analytics libraries (e.g. markouts, strategy analytics)</li><li>
Serve as a function-wide subject matter expert in one or more areas of focus</li><li>
Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle</li><li>
Influence peers and project decision-makers to consider the use and application of leading-edge technologies</li></ul>
Required Qualifications, Capabilities, And Skills<ul><li>
Design and implementation of front-office systems for quant trading.</li><li>
Hands-on practical experience delivering system design, application development, testing, and operational stability</li><li>
Strong expertise in Python. Comfortable with scientific &amp; dataset libraries such as pandas, numpy.</li><li>
Experience with KDB/Q</li><li>
Knowledge of data pipelines, market data processing and backtesting workflows.</li><li>
Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines</li><li>
Ability to tackle design and functionality problems independently with little to no oversight</li><li>
Proficiency in automation and continuous delivery methods</li><li>
In-depth knowledge of the financial services industry and their IT systems</li><li>
Academic experience in Computer Science, Computer Engineering, Mathematics, or a related technical field</li><li>
Knowledge of machine learning, statistical techniques and related libraries.</li></ul>
Preferred Qualifications, Skills And Capabilities<ul><li>
Strong knowledge and experience in FIX, Market Data, Analytics, OMS, and equities trading in global markets are assets</li><li>
Additional knowledge of Java / C++ is a strong plus.</li><li>
Practical cloud native experience is a plus.</li><li>
Practical cloud experience is a plus.</li></ul>
ABOUT US
 
 
J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. 
<div><div>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. 
</div></div> 
 
ABOUT THE TEAM
 
 
J.P. Morgan's Commercial &amp; Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial &amp; Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.</div>

Python Quant Data Engineer - Systematic Trading Technology

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