About this opportunity
Harnham lists this Research Engineer, GPU Performance opportunity in california, Missouri. Review the employer’s description below for duties, qualifications and application requirements.
Job description
We’re partnered with a well-funded AI research company focused on building next-generation multimodal models for media and interactive experiences. Their work spans cutting-edge generative systems and is increasingly moving toward real-time, interactive environments, pushing beyond static outputs into dynamic, AI-driven applications.
This is a deeply technical, high-impact role focused on making large-scale AI systems faster, more efficient, and capable of running in real time. You’ll work across the stack, from low-level GPU kernels to distributed training systems, directly influencing what is computationally possible for next-generation AI models.
What You’ll Do
Optimize training throughput across large GPU clusters, improving efficiency and utilization
Implement techniques such as mixed precision (FP8, BF16), memory-efficient attention, and checkpointing
Design and scale distributed training systems (tensor parallelism, FSDP, multi-node setups)
Profile and optimize inference pipelines for real-time multimodal generation
Improve latency through CUDA graphing, KV cache optimization, and operator fusion
Contribute across the stack, from kernel-level optimization to system-level architecture
Requirements
4+ years of experience in systems engineering, ML infrastructure, or performance optimization
Strong experience with GPU programming (CUDA, Triton, or similar)
Experience with distributed systems and large-scale training (NCCL, model parallelism)
Familiarity with ML framework internals such as PyTorch or JAX
Experience with mixed or low-precision techniques (FP8, INT8, BF16)
Proven experience building and operating scalable, fault-tolerant training systems
Strong interest in pushing the limits of performance for cutting-edge AI systems
Nice to Have
Experience with compiler optimizations or model compilation (e.g., PyTorch compile)
Background working on large multimodal or generative models
Exposure to real-time inference systems
If you're interested in working on the systems that enable next-generation AI models to train faster and run in real time, this is a rare opportunity to operate at the cutting edge of research and infrastructure.
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Worksite address
california, MO, 65018, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.