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Staff Machine Learning Infrastructure Engineer
$224K–$280K
San Francisco, CA +1 · Staff · Full-time · Posted Jul 1, 2026
- Location
- San Francisco, CA
San Francisco, California, United States - Experience
- 8+ years of professional software engineering career experience.
About the job
Build and scale cloud-agnostic ML training and validation infrastructure for autonomous transport models, supporting distributed GPU workloads and vehicle-data pipelines.
What you'll do
- Design and scale Kubernetes-based infrastructure for distributed GPU training and concurrent ML jobs.
- Build MLOps, experiment tracking, and high-throughput data pipelines for multi-sensor vehicle logs.
- Develop scalable autonomous-model validation and continuous integration testing, collaborating with robotics and ML teams.
What they ask for
- 8+ years of professional software engineering experience.
- Strong backend systems programming skills with proficiency in Go, Python, Java, or similar.
- Proficiency with Kubernetes and building cloud-agnostic environments from scratch.
- Experience with distributed ML compute frameworks, MLOps pipelines, model registries, and high-throughput data pipelines.
Nice to have
- Familiarity or exposure to Rust.
Pay
$224,000 - $280,000 per year (base salary)
Tools
Kubernetes, Go, Python, Java, Rust, Ray, MLflow
AI focus
Build infrastructure for training, tracking, and validating autonomous transport models and neural networks using distributed GPU workloads and vehicle data.
Model-written summary · the original posting is authoritative