Research Engineer, Training & Inference

About the Company

At Harmonic, we are building a mathematical reasoning engine that operates with absolute precision. While most AI makes maximum-likelihood guesses, Harmonic's Aristotle uses Lean 4 and reinforcement learning to verify its reasoning and results.

Responsibilities

• Maintain and optimize proprietary RL training and serving infrastructure
• Maximize throughput of reinforcement learning systems from data generation to model training
• Optimize inference stack for high-throughput RL and low-latency LLM production traffic
• Identify and resolve performance bottlenecks within distributed clusters
• Balance memory constraints with compute-heavy training cycles for multi-billion parameter models

Requirements

• BS in Computer Science or a related technical field
• 2+ years of relevant industry experience
• Proficiency in Python
• Experience with ML frameworks such as PyTorch, JAX, or TensorFlow
• Understanding of distributed training concepts and collective communication primitives (e.g., NCCL) OR practical experience deploying and profiling models on GPU-accelerated cloud infrastructure

Preferred Qualifications

• MS or PhD in Computer Science, Mathematics, or a related field
• 5+ years of relevant industry experience
• Proficiency in C++
• Experience writing or improving kernels (Triton, CuTeDSL, TileLang, CUDA, CUTLASS, ThunderKittens)
• Success deploying performant inference at scale
• Experience with FSDP, Tensor Parallelism, or related sharding techniques
• Experience designing RL systems for high-throughput training and asynchronous data sampling

Benefits

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