Software Engineer, RL Training Infra
WFA Digital Insight
The demand for skilled software engineers in AI research and deployment is skyrocketing, with a 25% increase in job postings in the last year alone. OpenAI is at the forefront of this revolution, and this role offers a unique chance to contribute to cutting-edge projects. As a software engineer for RL training infra, you'll need to possess a strong foundation in reinforcement learning, excellent debugging skills, and the ability to work across multiple engineering and infrastructure problems. With the company's commitment to safety and human needs, this is an exciting opportunity for those passionate about AI's potential to benefit humanity. Before applying, candidates should be prepared to demonstrate their expertise in ML infrastructure and their ability to thrive in a fast-paced environment.
Job Description
About the Role
The Software Engineer for RL Training Infra role at OpenAI is a critical position that focuses on keeping the company's frontier RL training runs fast, reliable, and unblocked. As a key member of the Post-Training Frontiers team, you will work on shepherding integrations, babysitting and scaling final runs, and building research and infra for horizontal integrations. Your primary responsibility will be to address engineering and infrastructure problems as they emerge, ensuring the team can deliver high-quality models.The Post-Training Frontiers team is responsible for creating the frontier agents OpenAI ships to the world, including those used in Codex, ChatGPT, and the API. Your work will have a direct impact on the company's ability to deploy AI systems that benefit humanity.
In this role, you will collaborate closely with research, infrastructure, and partner teams to overcome complex technical challenges. Your ability to learn quickly, debug deeply, and communicate effectively will be essential in this fast-paced environment.
What You Will Do
- Keep large-scale RL training runs moving by addressing urgent engineering and infrastructure problems
- Debug issues across training systems, inference, orchestration, scaling, and distributed infrastructure
- Solve hard technical problems at the boundary between research and engineering, such as scaling experiments and improving training reliability
- Improve reliability and efficiency for RL training runs
- Help researchers who are developing infra-heavy integrations, such as multi-agent capabilities or memory
- Turn recurring operational issues into better tools, systems, processes, or abstractions
- Work closely with research, infrastructure, and partner teams during tight model run timelines
- Become useful quickly in messy, ambiguous areas where ownership matters more than a perfectly scoped project
- Debug failures that cut across model behavior, training data, RL systems, evaluation infrastructure, serving systems, and agent harnesses
- Develop durable improvements and fixes based on your debugging efforts
What We Are Looking For
- A strong generalist engineer with experience in some layer of ML infrastructure
- Experience working on RL, inference, scaling, training systems, orchestration, or adjacent ML infrastructure
- The ability to learn extremely quickly and operate across unfamiliar layers
- Strong debugging skills with high ownership, low ego, and excellent communication
- Experience working in fast-moving environments where reliability, speed, and judgment matter
- A background in performance optimization, scaling, or production-critical infrastructure
- Experience supporting large-scale model training, async RL systems, or high-throughput ML infrastructure
- Familiarity with Excel and other relevant tools
- A strong foundation in software engineering principles and practices
Nice to Have
- Experience debugging distributed systems across GPUs, networking, orchestration, or inference stacks
- Background in working directly with researchers or fast-moving model teams
- Experience with load-bearing systems and processes
- Knowledge of current trends and advancements in AI research and deployment
Benefits and Perks
- The opportunity to work on cutting-edge AI projects with a talented team
- A competitive compensation package
- Equity in a leading AI research and deployment company
- Generous PTO and holiday schedule
- Comprehensive health insurance and benefits
- Remote work stipend and support for home office setup
- Professional development opportunities and access to industry conferences and events
- A culture that values safety, human needs, and the responsible development of AI systems
How to Stand Out
- Tip: When applying for this role, make sure to highlight your experience with ML infrastructure and your ability to learn quickly in a fast-paced environment.
- Develop a strong portfolio that showcases your debugging skills and experience with large-scale systems.
- To stand out, emphasize your understanding of the current trends and advancements in AI research and deployment.
- Be prepared to demonstrate your ability to communicate complex technical concepts to both technical and non-technical stakeholders.
- Research OpenAI's current projects and initiatives to show your passion for the company's mission and values.
- Familiarize yourself with common interview questions for software engineering roles in AI research and practice your responses.
- Consider reaching out to current or former employees to gain insights into the company culture and the role's responsibilities.
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