Real-Time AI GPU Performance Engineer

Harnham; California; On-site; USD 180,000 - 240,000; Full time

Job summary

Harnham collaborates with a well-funded AI research client in California to advance multimodal models. This highly technical role focuses on making large-scale AI systems faster, more efficient, and capable of real-time operation, spanning from GPU kernels to distributed training.

You will optimize throughput, implement FP8/BF16, memory-efficient attention, and checkpointing, profile inference pipelines, and push latency down with CUDA graphing and operator fusion, shaping the future of

Qualifications

• 4+ years of experience in ML systems, infrastructure, or performance optimization.
• Strong GPU programming experience with modern frameworks.
• Distributed training experience across large clusters.
• Familiarity with PyTorch or JAX internals.

Responsibilities

• Optimize training throughput across large GPU clusters.
• Implement mixed precision (FP8, BF16), memory-efficient attention, and checkpointing.
• Design and scale distributed training systems (tensor parallelism, FSDP, multi-node).
• Profile and optimize inference pipelines for real-time multimodal generation.
• Improve latency via CUDA graphing, KV cache optimization, and operator fusion.

Skills

CUDA

Triton

GPU programming

Distributed training

PyTorch/JAX internals

Mixed precision (FP8/BF16)

Education

Bachelors or Masters in CS/EE

Tools

CUDA toolkit

PyTorch

JAX

NCCL

Job description

Harnham collaborates with a well-funded AI research client in California to advance multimodal models. This highly technical role focuses on making large-scale AI systems faster, more efficient, and capable of real-time operation, spanning from GPU kernels to distributed training.

You will optimize throughput, implement FP8/BF16, memory-efficient attention, and checkpointing, profile inference pipelines, and push latency down with CUDA graphing and operator fusion, shaping the future of