Senior ML Infra Engineer - GPU Compute Leader

Flexion Robotics; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive Compensation

Leading robotics team

Energetic culture

Job summary

Flexion Robotics seeks a senior ML engineer to own GPU compute platforms and optimize training workflows on multi-node clusters. You will collaborate with AI and infrastructure teams to improve speed, utilization, and scalability across on-prem and cloud environments.

The role requires deep knowledge of distributed training, PyTorch, and Python, with hands-on experience in large-model training and performance profiling. This is an on-site opportunity in San Francisco with leadership exposure.

Qualifications

• Degree in CS/EE/Software Eng or equivalent practical experience.
• Hands-on with training/inference of large models on distributed multi-node GPU hardware.
• Proficiency in Python and working knowledge of PyTorch.
• Deep understanding of distributed training concepts (DDP, FSDP, NCCL).
• Experience with at least one cloud platform (AWS, GCP, Azure) or large-scale on-premises GPU infra.
• Experience with job scheduling/orchestration tools: Slurm and/or Kubernetes.

Responsibilities

• Architect and run cloud-based GPU clusters; select frameworks and tooling.
• Help AI engineers optimize training workloads and hardware utilization.
• Contribute to GPU compute strategies with AI engineering teams.
• Explore multi-cloud strategies to optimize capacity and cost.
• Raise engineering quality with testing, docs, and reliability.

Skills

Python

PyTorch

Distributed training

Cloud platforms

Slurm/Kubernetes

Large models

Education

Degree in Computer Science / Electrical Engineering / Software Engineering

Tools

Kubernetes

Slurm

Terraform

Ansible

Job description

Flexion Robotics seeks a senior ML engineer to own GPU compute platforms and optimize training workflows on multi-node clusters. You will collaborate with AI and infrastructure teams to improve speed, utilization, and scalability across on-prem and cloud environments.

The role requires deep knowledge of distributed training, PyTorch, and Python, with hands-on experience in large-model training and performance profiling. This is an on-site opportunity in San Francisco with leadership exposure.