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

Senior ML Infrastructure Engineer (PyTorch Kubernetes GPU Training)

We are seeking a Senior ML Infrastructure Engineer to design and scale the infrastructure powering large-scale machine learning training this role youll 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/GCP) 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.