Senior / Staff Ml Ops Engineer
- Countries
- Hybrid, Remote, USA, Canada
- Job Types
- software & tech, operations
- Experience
- expert
- Organization Types
- transportation & mobility, robotics, startups
- Location
- San Francisco - Dallas - Pittsburgh - Phoenix - Toronto - Hybrid - Remote
- Posted Date
- Company
- Waabi
- Salary
- USD184,000.0 - USD272,000.0 yearly
AI Summary
Job Description
Build and evolve training infrastructure on Kubernetes, focusing on GPU scheduling, autoscaling, and reliability. Shape developer-facing tools like CLIs and SDKs to shorten iteration cycles. Improve data and artifact layers for efficient GPU utilization. Implement CI/CD for models and build observability across the ML stack. Provide excellent documentation and support to drive tool adoption.
Hard Requirements
- 5+ years of software or infrastructure engineering experience
- Hands-on Kubernetes expertise (GPU scheduling, autoscaling, Helm)
- Excellent Python skills and experience designing APIs/CLIs
- Practical AWS depth (object storage, IAM, GPU compute, networking, IaC)
- Distributed training in PyTorch (DDP, FSDP)
- Experience with experiment tracking and model registry tooling
- Fluency with containers, CI/CD, and modern build systems
- Ability to influence without authority
- Collaborative mindset
- User empathy
- Strong product instincts, writing skills, and comfort with ambiguity
- Passion for self-driving technologies and frontier AI
Soft Requirements
- Internal developer platform, research platform, or DevEx work experience
- Large-scale distributed GPU training experience
- High-throughput loading of LiDAR or camera data
- Experience with workflow and scheduling systems
- Build-system depth (Bazel or similar)
- Simulation infrastructure or large-scale batch evaluation pipelines
- Background in ML, robotics, or autonomous systems infrastructure
- Experience in security- and IP-sensitive production environments
- Open-source contributions to ML infrastructure or developer tools