Machine Learning Engineering TL, Behavior Planning

Aurora

Pittsburgh, Pennsylvania

Full Time

Posted April 16, 2026

$171k - $247k

Not Specified

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Job Description

The ML Engineering TL at Aurora leads the development and deployment of advanced machine learning models for autonomous vehicle behavior planning, working on onboard and offboard systems, simulation engines, and safety evaluation, while mentoring engineering teams to push the boundaries of self-driving technology.

Key Responsibilities

Define the architecture of onboard planning models
Develop and deploy large-scale models using Imitation Learning and Reinforcement Learning for autonomous navigation
Build and enhance simulation engines and realistic world models for testing autonomous driving systems
Create offboard critic models to evaluate driving behavior at scale for safety and comfort
Push forward research to advance autonomous driving technology and deploy models in real-world vehicles
Mentor and lead junior engineers, guiding the ML planning ecosystem and long-term technical roadmap

Requirements

MS or PhD in Robotics, Machine Learning, Computer Science, or a related quantitative field, or equivalent practical experience.
8 years of experience developing state-of-the-art ML models, either in a research or production setting.
Hands-on experience working on Imitation Learning or Reinforcement Learning applied to physical or simulated agents.
Experience training large models on massive datasets using distributed computing.
Fluency in Python, with a focus on writing high-performance, maintainable code.
Deep experience with PyTorch preferred or another modern ML framework, and a mastery of modern ML architectures including Transformers and Diffusion Models.
Ability to define the architecture of onboard planning models for autonomous vehicles.
Experience developing and deploying large-scale models trained with Imitation Learning and Reinforcement Learning that enable autonomous navigation in complex environments.
Experience building simulation engines and developing offboard foundation models that create realistic world models for testing autonomous driving systems.
Experience developing powerful offboard critic models that evaluate driving behavior at scale, identifying subtle nuances in comfort, progress, and safety.
Experience deploying complex ML systems in production environments that meet high safety standards.
Experience pushing forward the state-of-the-art in autonomous driving technology and bridging research and production deployment.
Experience mentoring and leading junior engineers and shaping the long-term ML roadmap, including onboard and offboard ecosystems.