Senior ML Infrastructure Engineer (PyTorch, Kubernetes, GPU Training)

Senior ML Infrastructure Engineer (PyTorch, Kubernetes, GPU Training) Short Job Description We are seeking a Senior ML Infrastructure Engineer to design and scale the infrastructure powering large-scale machine learning training workloads. In this role, you'll build high-performance GPU training platforms, optimize distributed training pipelines, and improve the developer experience for ML researchers.

Responsibilities:

Design and scale distributed ML training infrastructure for large GPU clusters. Build and optimize training pipelines using PyTorch , DeepSpeed , and distributed training frameworks. Develop and maintain job scheduling systems using Kubernetes and/or SLURM . Create high-throughput data pipelines for large-scale multimodal datasets. Optimize GPU utilization, memory efficiency, and overall system performance. Build low-latency inference pipelines for production ML deployments.

Required Skills:

7+ years of experience in ML Infrastructure, HPC, or Distributed Systems. Strong experience with PyTorch , DeepSpeed , FSDP , ZeRO , or similar distributed training frameworks. Hands-on experience with Kubernetes , cloud platforms ( AWS/Google Cloud Platform ), and containerized environments. Strong understanding of distributed systems, GPU optimization, NCCL, memory management, and performance tuning. Experience building scalable ML infrastructure from development through production.

Location:

Redwood City, CA (On-site)

Employment Type:

Full-Time Nice to

Have:

Experience with multimodal AI, robotics data pipelines, Triton, TensorRT, custom ML kernels, or ML compiler/runtime optimization.