Description:

Build and evolve training infrastructure on Kubernetes, focusing on GPU scheduling, autoscaling, and reliability.
Develop user-friendly tools and workflows for ML teams, enhancing efficiency and reducing latency.
Strengthen data handling and artifact management for large multimodal datasets.
Create durable tooling from one-off Python scripts, ensuring documentation and observability.
Implement CI/CD for models and enhance observability across the ML stack.
Drive user adoption through prototyping and iterative feedback.
Ensure platform reliability and establish security and governance guardrails.

Requirements:

5+ years in software or infrastructure engineering, particularly in ML or data-intensive systems.
Proficient in Python with experience in designing APIs and CLIs.
Practical knowledge of AWS services, distributed training in PyTorch, and CI/CD processes.
Strong collaboration skills and user empathy, with a focus on impactful problem-solving.
Ability to influence and persuade senior engineers, with strong writing and product instincts.
Passionate about self-driving technologies and AI infrastructure.

Competitive salary range of $184,000 - $272,000, plus equity and performance bonuses.
Comprehensive health benefits including medical, dental, and vision coverage.
Unlimited vacation policy and flexible work hours.
Daily snacks, drinks, and catered meals in the office.
Regular team-building activities and social events.

Skills