Member of Technical Staff, Research Engineer (GPU Performance)
Remote$270k - $370kfull-timePosted 11mo ago
About the role
Seeking a Research Engineer to optimize and scale GPU-based training and inference systems for AI world models, enhancing efficiency without compromising capabilities.
Responsibilities
- Optimize training throughput across large GPU clusters using custom kernels, mixed-precision strategies, memory-efficient attention, and activation checkpointing.
- Design and maintain distributed training infrastructure: tensor parallelism, context parallelism, FSDP, fault-tolerant multi-node setups.
- Profile and accelerate inference pipelines for real-time multimodal generation — CUDA graph compilation, KV cache optimization, operator fusion, latency reduction.
- Optimize and scale training infrastructure for improved efficiency and reliability.
- Contribute to low-level kernel optimizations and high-level model design.
Qualifications
- 4+ years of experience in systems engineering, ML infrastructure, or performance optimization for deep learning.
- Familiarity with GPU kernel development (CUDA, Triton, CUTLASS) and distributed systems (NCCL, collective communication, model parallelism).
- Experience with ML framework internals (PyTorch, JAX) and mixed-precision / low-precision techniques (FP8, INT8).
- Experience building and operating large-scale training infrastructure, including fault tolerance and cluster orchestration.
Skills
Required:GPU kernel developmentDistributed systemsML frameworksMixed-precision techniques
Last updated Apr 6, 2026. Interested? Apply on the company site.
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