At a Glance
- Tasks: Design and optimise software for large-scale machine learning workloads across supercomputers.
- Company: Join OpenAI, a leader in AI research and deployment.
- Benefits: Hybrid work model, relocation assistance, and a focus on professional growth.
- Other info: Dynamic environment with opportunities for innovation and career advancement.
- Why this job: Work at the forefront of AI, tackling complex challenges and making a real impact.
- Qualifications: Experience in distributed systems, proficient in Python and Rust, strong engineering skills.
The predicted salary is between 60000 - 80000 £ per year.
About the Team
Training Runtime designs the core distributed runtime that powers everything from early research experiments to frontier-scale model runs. We work on building robust, scalable, high performance components to support our distributed training workloads. Our priorities are to maximize the productivity of our researchers and our hardware, with the goal of accelerating progress towards AGI. Within Training Runtime, the Process Management team develops the distributed OS responsible for launching, coordinating, and supervising the large numbers of processes that make up modern training workloads. Our runtime sits beneath training frameworks and on top of research infrastructure, ensuring jobs run reliably across massive clusters while maintaining performance, stability, and observability. Success for us is measured by both system reliability and researcher velocity - enabling ideas to scale from experiments to production training runs.
About the Role
As a Training Runtime: Process Management Engineer, you will work on the software that ties thousands of computers together and exposes them as a unified system. This system has to serve individual researchers running multiple parallel experiments, as well as our largest training runs spanning hundreds of thousands and even millions of machines and accelerators. This requires easy to use, introspectable systems that can promote a fast debugging and development cycle, as well as relentless optimization for scale while maintaining stability and performance throughout. You will work primarily in Rust, building high-performance asynchronous systems with a strong emphasis on performance, correctness, and scalability. Working at this scale and at the frontier of AI development poses novel challenges. Out-of-the-box approaches often don’t work. The problems you will be working on are highly ambiguous and require strong design judgment as well as proficient execution to advance the state of our infrastructure. We’re looking for people who love optimizing an end-to-end platform, understanding high-performance architectures to maximize both local and distributed performance across our supercomputers. We’re looking for engineers excited by the rapid pace of responding to the dynamic and evolving needs of our training runtime and compute stack.
This role is based in London, UK. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
In this role, you will:
- Work across our Python and Rust stack
- Design, build, and maintain software to orchestrate and monitor machine learning workloads on our largest supercomputers
- Profile and optimize our software stack to support computation orchestration at frontier scale
- Improve reliability, observability, and fault tolerance for long-running jobs
- Debug complex distributed systems issues across large clusters
- Respond to the changing shapes and needs of the ML systems to enable our researchers
You might thrive in this role if you:
- Have experience developing distributed systems (not just operating them)
- Enjoy understanding how large systems behave and fail at scale
- Care deeply about performance, correctness, and reliability
- Have strong software engineering skills and are proficient in Python and Rust or another systems programming language (e.g. C++)
- Have solid Linux knowledge, and are comfortable with systems-level debugging, performance analysis, and memory profiling
- Are comfortable and experienced working and developing asynchronous and concurrent systems
- Like high-ownership environments with light process and strong engineering agency
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, colour, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
Training, Process Management Engineer employer: Slope
OpenAI is an exceptional employer, offering a dynamic work environment in London that fosters innovation and collaboration. With a hybrid work model and a strong emphasis on employee growth, we provide opportunities for engineers to tackle complex challenges at the forefront of AI development while enjoying a culture that values diverse perspectives and encourages high ownership. Our commitment to safety and human-centric AI ensures that your contributions will have a meaningful impact on the world.
StudySmarter Expert Advice🤫
We think this is how you could land Training, Process Management Engineer
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the industry. Attend meetups, conferences, or even online webinars. You never know who might have the inside scoop on job openings or can refer you directly to hiring managers.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to distributed systems or high-performance computing. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for technical interviews by practicing coding challenges and system design problems. Use platforms like LeetCode or HackerRank to sharpen your skills. Remember, they want to see how you think and solve problems, so talk through your thought process!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re genuinely interested in joining our team. Tailor your application to highlight your experience with Rust and distributed systems – that’s what we’re all about!
We think you need these skills to ace Training, Process Management Engineer
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Training Runtime: Process Management Engineer role. Highlight your experience with distributed systems and any relevant projects you've worked on, especially those involving Rust or Python.
Showcase Your Skills:Don’t just list your skills; demonstrate them! Use specific examples from your past work that showcase your proficiency in high-performance architectures and your ability to optimise systems at scale.
Be Clear and Concise:When writing your application, keep it clear and to the point. Avoid jargon unless it's necessary, and make sure your passion for AI and system optimisation shines through. We want to see your personality!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details you need about the role and our team!
How to prepare for a job interview at Slope
✨Know Your Tech Stack
Make sure you’re well-versed in both Python and Rust, as these are key to the role. Brush up on your knowledge of distributed systems and be ready to discuss how you've tackled performance and reliability challenges in past projects.
✨Understand the Big Picture
Familiarise yourself with the concepts of high-performance computing and how large systems operate. Be prepared to talk about how you would approach debugging complex issues across massive clusters and what strategies you’d use to optimise performance.
✨Show Your Problem-Solving Skills
Expect to face ambiguous problems during the interview. Think through your thought process and be ready to demonstrate your design judgement. Share examples of how you’ve approached similar challenges in the past and what solutions you implemented.
✨Emphasise Collaboration and Adaptability
This role requires working closely with researchers and adapting to their evolving needs. Highlight your experience in collaborative environments and how you’ve successfully navigated changes in project requirements or priorities.
