Research Engineer, Training & Inference

About this role

Harmonic seeks a Research Engineer to optimize and maintain their custom reinforcement learning and inference stack, from Python APIs to CUDA kernels. You'll focus on maximizing throughput for training and serving high-performance AI systems that solve complex mathematical reasoning problems.

What you'll do

• Maintain and optimize proprietary RL training and inference infrastructure across all layers
• Maximize reinforcement learning throughput through sharded multi-node training and inference optimization
• Optimize inference serving stack for high-throughput RL and low-latency LLM production traffic
• Identify and resolve performance bottlenecks in distributed clusters for multi-billion parameter models
• Write or improve custom kernels to resolve low-level compute bottlenecks
• Design systems balancing memory efficiency with compute-intensive training cycles

What they're looking for

• Python proficiency
• ML framework experience (PyTorch, JAX, or TensorFlow)
• Distributed training concepts and collective communication (NCCL)
• GPU-accelerated infrastructure deployment and profiling
• C++ (preferred)
• Custom kernel development (Triton, CUDA, CUTLASS)
• Multi-node GPU cluster scaling (FSDP, Tensor Parallelism)
• Reinforcement learning system design

Benefits

• Unlimited PTO
• 401(k) matching
• 100% employer-paid health, vision, and dental for employees; 50% for dependents
• Health Savings Account (HSA) available
• Work on cutting-edge mathematical reasoning AI
• Elite technical team environment