What are the responsibilities and job description for the Senior ML Infrastructure Engineer (PyTorch, Kubernetes, GPU Training) position at Finoit Inc.?
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.
Salary : $250,000 - $320,000