Job Description

Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We’re searching for a Machine Learning Engineer – Models and Validation.

In this role, you will

• Build learned models used to develop and evaluate the Motion Planner in large scale simulations on complex scenarios.
• Build novel generative models to augment our simulation data and create unseen realistic and plausible scenarios for training and evaluating the Motion Planner and other sub-systems. 
• Support the various parts of the ML model development workflow, including dataset management, label quality, feature processing pipelines, model training and evaluation infrastructure.
• Work closely with Behavior Planning and Safety Engineering teams to understand their requirements and inform our technical approach.
• Work with the Behavior Simulation and Perception Simulation teams to add any necessary core functionality required to deploy our models.
• Develop tools to visualize, introspect and extract insights from running our models.
• Support your team with code reviews and ideas as part of a collaborative environment.

Required Qualifications

• Motivated and can work independently in a fast paced environment, navigating problem ambiguity.
• Strong knowledge of ML fundamentals.
• ML model building experience (data preparation, training, model design, evaluation)
• Robotics-related domain knowledge.
• Strong knowledge of PyTorch, Python, numpy and common data science libraries.
• 2+ years of industry experience in a related field.

Desirable Qualifications

• Experience with Motion Planner development.
• Experience deploying ML models in production environments.
• Project work, or publications in top-tier conferences or workshops.

The base salary range for this position is $139,000-$223,000 per year.  Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits. 

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