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About the Role
You will build the infrastructure for post-training and reinforcement learning, enabling the team to post-train models, benchmark them, and build and train agents. This includes training and post-training runs, inference serving, systems for RL environments, evaluation harnesses, and data pipelines. You will own the stack end-to-end, setting engineering standards and optimizing performance, scalability, and reliability.
Responsibilities
- Build infrastructure for post-training and reinforcement learning.
- Own the stack end-to-end, from design and implementation through deployment, documentation, and maintenance.
- Set engineering standards, including testing, observability, and versioning.
- Find and remove bottlenecks across training, rollout generation, evaluation runs, data movement, and GPU utilization.
- Improve the performance, scalability, and reliability of the stack.
- Report into the intelligence team and work closely with researchers and engineers.
Requirements
- Built and operated infrastructure that researchers relied on for training, serving, or reinforcement learning.
- Experience building infrastructure for LLM inference or RL training.
- Systems fundamentals, and the ability to profile a slow or failing workload down to its cause.
- Hands-on work with GPU clusters, distributed training, model serving, or high-throughput inference, including scheduling, utilization, and cost.
- Experience with Docker, Kubernetes, Ray, or Terraform, and long-running jobs with retries, checkpointing, and clear failure reporting.
- Good judgment on technical trade-offs.
- Clear writing and communication about system design, operational risk, and engineering trade-offs.
- Fluent Python and daily use of coding agents.
Skills
- LLM inference
- RL training
- Systems fundamentals
- GPU clusters
- Distributed training
- Model serving
- High-throughput inference
- Docker
- Kubernetes
- Ray
- Terraform
- Python
- Coding agents
- Inference engines (SGLang, vLLM)
- RL training frameworks (veRL, Miles, TRL)
- Agent environment and evaluation frameworks (Harbor)
- Sandboxed execution for agents (Firecracker, gVisor)
- Compilers
- Custom kernels
- Optimization for specific GPU hardware
- Data versioning
- Data lineage for ML datasets
Location
- London
- San Francisco
Work Type
- Full-time
Experience Level
- Mid-level
- Senior
Benefits
- 30 days of annual leave plus local public holidays
- Pension with 10% employer contribution (UK)
- Bupa private health cover (UK)
- 401(k) with matching (US)
- Health cover (US)
About the Company
- Stealth startup in AI and bio, building infrastructure for data generation.
- AI for biology has a data problem; building infrastructure to produce critical data with quality and traceability.
- Heads-down on execution, sharing more about the product after initial conversations.
Equal Opportunity
- Equal opportunity employer.
- Hiring decisions made on merit, scope-fit, and the strength of the working relationship.
- Applications welcome from candidates of any background.