At a Glance
- Tasks: Build and enhance the RL training infrastructure for faster research iterations.
- Company: Join a leading AI company focused on innovative machine learning solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on collaboration and innovation.
- Why this job: Make a real impact by improving research efficiency and reliability in machine learning.
- Qualifications: Strong software engineering skills and experience with ML infrastructure preferred.
The predicted salary is between 60000 - 80000 € per year.
Requirements
- Have strong software engineering fundamentals and a track record of building performant, reliable systems
- Have worked on ML infrastructure, distributed systems, or research tooling
- Care about enabling other people's work and find leverage through platforms rather than individual experiments
- Are comfortable operating across the stack, from low-level performance work to RL algorithms
- Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego
- (Desirable) Experience with large-scale distributed training (RL, pre-training, or post-training)
- (Desirable) Familiarity with JAX, PyTorch, or similar ML frameworks
- (Desirable) A track record of operating at the edge of research and infra in a fast-moving environment
- 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
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.
What the job involves
- The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs.
- As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster.
- This is high-leverage work: small improvements to velocity compound across every researcher and every run.
- Build and improve the RL training infrastructure that researchers depend on day-to-day.
- Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed.
- Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster.
- Own the reliability and performance of research runs end-to-end.
- Contribute to design decisions that shape how Anthropic does RL at scale.
Research Engineer (Machine Learning, Reinforcement Learning Velocity) employer: Deepstreamtech
At Anthropic, we pride ourselves on being an exceptional employer, particularly for those in the Research Engineer role focused on Machine Learning and Reinforcement Learning. Our collaborative work culture fosters innovation and encourages personal growth, allowing you to make impactful contributions while working alongside talented researchers and engineers. With a commitment to employee development and a supportive environment that values diverse perspectives, you'll find unique opportunities to advance your career in a fast-paced, cutting-edge field.
StudySmarter Expert Advice🤫
We think this is how you could land Research Engineer (Machine Learning, Reinforcement Learning Velocity)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML infrastructure or distributed systems. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts simply, as you'll need to communicate effectively with both researchers and engineers.
✨Tip Number 4
Don't hesitate to apply through our website! Even if you don't tick every box, we value diverse experiences and perspectives. If you're passionate about the role, go for it – you might just surprise yourself!
We think you need these skills to ace Research Engineer (Machine Learning, Reinforcement Learning Velocity)
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your software engineering fundamentals and any experience you've got with ML infrastructure or distributed systems. We want to see how you've built reliable systems in the past, so don’t hold back!
Be a Team Player:We care about enabling others, so share examples of how you've collaborated with teams or improved processes. If you've worked on platforms that help others succeed, let us know – it’s a big plus!
Keep It Relevant:Tailor your application to focus on your experience with RL algorithms and any familiarity with frameworks like JAX or PyTorch. Even if you don’t tick every box, we encourage you to apply – your unique background might just be what we need!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to see your application and get you into the process. We can’t wait to hear from you!
How to prepare for a job interview at Deepstreamtech
✨Know Your Tech Inside Out
Make sure you brush up on your software engineering fundamentals and be ready to discuss your experience with ML infrastructure and distributed systems. Be prepared to share specific examples of how you've built performant and reliable systems in the past.
✨Showcase Your Collaborative Spirit
This role is all about enabling others, so highlight your experiences where you've partnered with researchers or engineering teams. Talk about how you've identified pain points and contributed to solutions that made their work easier and faster.
✨Demonstrate Your Quick Iteration Mindset
The company values a bias towards shipping and iterating quickly. Share instances where you've had to pivot or adapt your approach rapidly, and how that led to successful outcomes. This will show that you're comfortable operating in a fast-moving environment.
✨Be Ready for Technical Challenges
Expect some technical questions related to RL algorithms and frameworks like JAX or PyTorch. Brush up on these topics and be ready to discuss your hands-on experience. If you have any projects or research that showcase your skills, don’t hesitate to bring them up!
