Software Engineer II, Controls Data & Simulation
Aurora Innovation
About the role
About
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 Software Engineer II to join the Control Simulation team and help transform how we validate and improve the safety and performance of the Aurora Driver.
In this role, you will operate at the intersection of Machine Learning, Software Engineering, Data Science, and Vehicle Dynamics. You will contribute to the development of our next‑generation data‑driven vehicle simulator, build data pipelines that source critical scenarios from our fleet and generate simulations from logs, and analyze data to prove the safety of our motion control software.
This is a high‑impact role for an engineer who enjoys a mix of model development, infrastructure building, and data analysis.
Responsibilities
- Develop Vehicle Models: Help design and implement our next‑generation differentiable vehicle dynamics simulator for controls V&V.
- Build Simulation Pipelines: Design and maintain the software pipelines required to generate “Sim‑from‑Log” scenarios. Ensure that high‑value on‑road events are systematically converted into reproducible simulation tests.
- Implement Control Scenario Taxonomies & Curate Data: Build the data structures and algorithms to categorize complex control scenarios (e.g., maneuvers, environments) and use this framework to source balanced datasets from on‑road logs, ensuring comprehensive ODD coverage for model training.
Required Qualifications
- BS or MS in Computer Science, Robotics, Data Science, or a related engineering field.
- 2+ years of software engineering experience with a focus on data‑intensive applications or machine learning.
- Strong proficiency in Python (including libraries like Pandas, NumPy, Scikit‑learn).
- Hands‑on experience with PyTorch or similar machine learning frameworks.
- Experience working with large datasets, SQL, and data processing pipelines.
- Strong analytical skills with the ability to translate complex data into actionable engineering insights.
- Background or interest in Control Theory or Vehicle Dynamics (understanding of kinematics, kinetics, and actuation).
Desirable Qualifications
- Experience with machine learning or system identification for physics‑based models.
- Familiarity with V&V methodologies, simulation frameworks, or log analysis tools.
- Experience building data visualization tools or dashboards to track software quality and coverage.
- Proficiency in C++ (necessary for integrating with on‑vehicle or production codebases).
Compensation & Benefits
- Base salary range: $126,000 – $181,000 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.
- Eligible for an annual bonus, equity compensation, and benefits.
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Requirements
- BS or MS in Computer Science, Robotics, Data Science, or a related engineering field.
- 2+ years of software engineering experience with a focus on data-intensive applications or machine learning.
- Strong proficiency in Python (including libraries like Pandas, NumPy, Scikit-learn).
- Hands-on experience with PyTorch or similar machine learning frameworks.
- Experience working with large datasets, SQL, and data processing pipelines.
- Strong analytical skills with the ability to translate complex data into actionable engineering insights.
- Background or interest in Control Theory or Vehicle Dynamics (understanding of kinematics, kinetics, and actuation).
Responsibilities
- Help design and implement our next-generation differentiable vehicle dynamics simulator for controls V&V.
- Design and maintain the software pipelines required to generate "Sim-from-Log" scenarios.
- Build the data structures and algorithms to categorize complex control scenarios and use this framework to source balanced datasets from on-road logs, ensuring comprehensive ODD coverage for model training.
Benefits
Skills
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