AI Infrastructure Engineer - Autonomy

Applied Intuition • Mountain View, CA

248d • $153,000 - $222,000

About The Position

We are looking for both infrastructure engineers with expertise in machine learning pipelines and ML engineers that want to work beyond modeling to join our AI Infrastructure group. This role will work across the entire AI lifecycle (dataset generation, training frameworks, compute, evaluation, and deployment) and work directly with modeling teams. This team is a good fit if you are excited to work on broad, ambiguous problems and develop across the entire ML stack. At Applied Intuition, we encourage all engineers to take ownership over technical and product decisions, closely interact with external and internal users to collect feedback, and contribute to a thoughtful, dynamic team culture.

Requirements

• Experience with building software components to address production, full-stack machine learning challenges.
• Opinions about building a company-wide platform for ML training, evaluation, and deployment
• Knowledge of the open source landscape with judgment on when to choose open source versus build in-house
• Excellent analytical and problem-solving skills

Responsibilities

• Design and build training, inference, and evaluation infrastructure to support our current autonomy stack development, orchestrating massive GPU clusters to process petabytes of multimodal sensor data
• Optimize multimodal data ingestion and preprocessing pipelines (LiDAR, camera, radar, map priors) to support cutting-edge perception and planning model development
• Work across cloud environments to support high-throughput distributed training
• Collaborate closely with the AI research team and autonomy teams

Benefits

• Base salary
• Equity in the form of options and/or restricted stock units
• Comprehensive health, dental, vision, life and disability insurance coverage
• 401k retirement benefits with employer match
• Learning and wellness stipends
• Paid time off

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Bachelor's degree
• Number of Employees: 501-1,000 employees