Harmonic AI
Research Engineer, Training & Inference
$200K–$450K
Palo Alto · On-site · Full-time · Posted May 7, 2026
- Experience
- 2+ years required; 5+ years preferred.
- Education
- BS in Computer Science or a related technical field, or equivalent industry experience; MS or PhD preferred.
About the job
Build and optimize Harmonic’s reinforcement learning training and inference infrastructure to maximize performance for AI agents and production LLM workloads.
What you'll do
- Maintain and optimize the RL training and serving stack, from Python APIs to CUDA kernels.
- Increase training throughput through data generation, sharded multi-node training, and inference algorithms.
- Improve inference performance and distributed-cluster throughput, latency, and memory efficiency.
What they ask for
- BS in Computer Science or a related technical field, or equivalent industry experience.
- 2+ years of relevant, hands-on industry experience.
- Proficiency in Python and experience building or maintaining components within ML frameworks such as PyTorch, JAX, or TensorFlow.
- Either understanding of distributed training concepts and collective communication primitives, or practical experience deploying and profiling models on GPU-accelerated cloud infrastructure.
Nice to have
- MS or PhD in Computer Science, Mathematics, or a related field; 5+ years of relevant, hands-on industry experience.
- Proficiency in C++, kernel optimization, large-scale inference, multi-node model sharding, or high-throughput reinforcement learning systems.
Pay
$200K – $450K (base salary)
Tools
Python, PyTorch, JAX, TensorFlow, NCCL, C++, CUDA, Triton
AI focus
Develop reinforcement learning systems and optimize training and inference for foundation models and production LLM workloads.
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