Agent Post-Training, Frontier Evals and Environments Research
Researcher building frontier RL environments, evaluations, and training signals to steer OpenAI's largest agent training runs and measure model capabilities.
Research Engineer, Discovery
Builds large-scale infrastructure for AI scientist training, evaluation, and deployment, resolving bottlenecks in distributed systems for scientific AGI. Requires 6+ years in infrastructure engineering with expertise in ML stacks, containers, and data pipelines.
350k – 850kSan Francisco, CAAI ResearchHybrid6+ YOE
About the role
Responsibilities
- Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
- Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
- Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI
- Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
- Collaborate to translate experimental requirements into production-ready infrastructure
- Develop large scale data pipelines to handle advanced language model training requirements
- Optimize large scale training and inference pipelines for stable and efficient reinforcement learning
You may be a good fit if you
- Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
- Are a strong communicator and enjoy working collaboratively
- Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
- Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
- Have proven track record of building large-scale data pipelines and distributed storage systems
- Excel at diagnosing and resolving complex infrastructure challenges in production environments
- Can work effectively across the full ML stack from data pipelines to performance optimization
- Have experience collaborating with other researchers to scale experimental ideas
- Thrive in fast-paced environments and can rapidly iterate from experimentation to production
Strong candidates may also have
- Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
- Background in building infrastructure for AI research labs or large-scale ML organizations
- Knowledge of GPU/TPU architectures and language model inference optimization
- Experience with cloud platforms (AWS, GCP) at enterprise scale
- Familiarity with VM and container orchestration
- Experience with workflow orchestration tools and experiment management systems
- History working with large scale reinforcement learning
- Comfort with large scale data pipelines (Beam, Spark, Dask)
Annual Salary: $350,000 — $850,000 USD
Education requirements: At least a Bachelor's degree in a related field or equivalent experience.
Skills
KubernetesDockerPyTorchJAXAWSGCPApache BeamSparkDaskDistributed Systems
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