Staff Machine Learning Infrastructure Engineer
CloudKitchens12 days ago
Base Salary
$224k - $280k/yr
Responsibilities
- Design and scale repeatable Kubernetes-based machine learning infrastructure for distributed GPU training.
- Implement distributed compute orchestration for concurrent ML training jobs across large GPU clusters.
- Integrate experiment tracking, metadata, model management, and MLOps tooling for training observability.
- Build and optimize high-throughput data ingestion pipelines for petabyte-scale multi-sensor vehicle logs.
- Architect autonomous model validation and continuous integration infrastructure for regression-free releases.
- Partner with robotics engineers and ML researchers to improve training and deploy-to-vehicle workflows.
Requirements
- 8+ years of professional software engineering experience.
- Strong backend systems programming skills with proficiency in Go, Python, Java, or similar languages.
- Proficiency with Kubernetes and experience building cloud-agnostic environments from scratch.
- Experience implementing distributed ML compute frameworks such as Ray for large multi-node GPU workloads.
- Hands-on experience building MLOps pipelines, metadata tracking architectures, and model registries using platforms such as MLflow.
- Experience managing high-throughput data pipelines with modern distributed data engines.
- Rust familiarity or exposure is a plus.
Benefits
- Medical, dental, vision, disability, and life insurance.
- Flexible Spending Account and Health Savings Account options.
- 401(k) and equity eligibility.
- Sick time, unlimited flexible time off, paid holidays, and paid parental leave.
- Pre-tax commuter benefit plan.
- Team lunch in the SoMa office every Tuesday and Thursday.
- Based in the San Francisco office and onsite five days per week for office-based teams.
Tech Stack
Categories
About CloudKitchens
We provide kitchen infrastructure and software that empower food & beverage operators to expand their operations with minimal upfront capital and time.