Design and build training and evaluation infrastructure using large GPU clusters.
Build benchmarking and evaluation systems for model performance.
Develop data sampling, dataset generation, and data curation pipelines.
Enable high-throughput distributed training across cloud environments.
Collaborate with AI research/autonomy/platform teams to productionize research.

🎯 Requirements

Production ML systems across training, evaluation, data, deployment.
Experience building an ML platform for training/eval/deploy.
Performance engineering for large-scale ML training; profiling and optimization.
Systems-level debugging for distributed training.
Deep familiarity with open-source ML/systems; when to adopt vs build.
Technical experience: PyTorch, CUDA, Ray, Flyte, Kubernetes.

🎁 Benefits

Health, dental, vision, life and disability insurance
401k with employer match
Learning and wellness stipends
Paid time off
Equal opportunity employer/notice provisions

Apply on employer's website

This employer gathers applications via their own applicant tracking system.

You will be redirected to an external application form.

Share job

Meet JobCopilot: Your Personal AI Job Hunter

Automatically Apply to Engineering Jobs. Just set your preferences and Job Copilot will do the rest — finding, filtering, and applying while you focus on what matters.

Activate JobCopilot