Research Engineer, Training & Inference

Highlights

• Own the full RL stack from environment simulators to distributed training loops.
• Build the verification engine that catches bugs before code ships.
• Maximize throughput for RL training and low-latency production serving.

What they offer

• Unlimited PTO
• 401(k) matching
• 100% employer-paid health, vision, and dental benefits for employees and 50% coverage for dependents
• Health Savings Account (HSA) available for qualifying health plans

Ideal candidate

Systems-obsessed engineer who owns the full stack and chases hardware-level performance. You thrive refactoring deep infrastructure to maximize throughput for foundation-model workloads.

Requirements

• BS in Computer Science or related technical field or equivalent industry experience
• 2+ years of relevant, hands-on industry experience
• Proficiency in Python
• Experience building or maintaining components within ML frameworks such as PyTorch, JAX, or TensorFlow
• Understanding of distributed training concepts and collective communication primitives such as NCCL, or experience deploying and profiling models on GPU-accelerated cloud infrastructure
• MS or PhD in Computer Science, Mathematics, or related field (preferred)
• 5+ years of relevant, hands-on industry experience (preferred)
• Proficiency in C++ (preferred)
• Experience writing or improving kernels to resolve low-level bottlenecks (preferred)
• Proven success deploying performant inference at scale (preferred)
• Direct experience scaling models via FSDP, Tensor Parallelism, or related sharding techniques on multi-node GPU clusters (preferred)
• Experience designing reinforcement learning systems for high-throughput training and asynchronous data sampling (preferred)