Research Engineer, Infrastructure (Remote - Worldwide)

This role sits within a fast-growing AI company that applies frontier agentic AI to professional services and knowledge work. The team has recently post-trained its first model built for legal work and is now expanding its post-training efforts. You will join a small, high-bar group of hands-on engineers and researchers, where each person owns a large part of the stack.

What you'll do

• Scale post-training infrastructure supporting supervised fine-tuning and reinforcement learning on leading open-weight models.
• Build distributed rollout systems that coordinate model inference, tool execution, and collection of training experience for long-horizon tasks.
• Create reproducible agent environments where models can interact with documents, tools, and external systems during training and evaluation.
• Own the performance and reliability of training runs, including GPU utilization, checkpointing, failure recovery, and observability.
• Build internal tools for training data versioning, running and browsing evals, and launching and tracking experiments.
• Partner with researchers to turn experimental workflows into dependable systems while keeping room for rapid iteration.

What you need

• Experience building ML infrastructure, distributed systems, or research tooling, with ownership of performance and reliability.
• Strong Python and generalist software engineering skills across backend services, data systems, and internal tools.
• Familiarity with PyTorch, JAX, or similar ML frameworks.
• Hands-on experience with distributed training or inference, including diagnosing bottlenecks and failures across the stack.
• Practical judgment about what to build versus adopt, and the ability to ship quickly as needs change.
• Experience with RL rollouts, agent environments, or sandboxed tool execution is a plus.

Open to applicants anywhere in the world.

How to apply

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