Senior Software Systems Engineer, Autonomous Systems Validation Confidence

Job Description

We are looking for a Senior Software Systems Engineer to develop the methods software and quantitative evidence used to measure confidence in autonomous vehicle validation results.

This role sits at the intersection of software engineering systems engineering simulation statistics and data science. You will help answer questions such as: Do our tests provide sufficient coverage Are our metrics meaningful Can simulation results predict real-world performance How confidently can we detect a regression or support a release decision

This role is a strong fit for someone who has experience with both programming and physical or cyber-physical systems such as autonomous vehicles robotics aerospace industrial systems or other complex engineered products.

• Develop scalable frameworks and methods for measuring validation confidence across simulation and real-world testing including coverage sampling metric quality statistical significance and regression detection.

• Build tools and data pipelines for test execution analysis metric computation scorecards and confidence reporting.

• Evaluate simulation validity and road predictive power using measurable defensible criteria; analyze test and vehicle data to identify uncertainty pipeline issues regressions and gaps in evidence.

• Translate validation claims and release questions into requirements experiments test suites metrics and quantitative decision criteria.

• Improve the throughput repeatability and quality of validation workflows.

• Partner with simulation safety autonomy and release teams and communicate conclusions assumptions limitations and recommendations clearly.

• Strong programming skills in Python C or a comparable language with experience writing clear testable maintainable code.

• Experience applying engineering or quantitative methods to a physical cyber-physical or other real-world system.

• Ability to turn ambiguous validation questions into measurable requirements metrics experiments or decision criteria and investigate complex behavior using incomplete or noisy data.

• Ability to collaborate across disciplines and explain technical results with appropriate precision and context.

• Bachelors degree in engineering physics applied mathematics statistics data science or a related technical field or equivalent practical experience.

• Experience with autonomous vehicles robotics simulation aerospace industrial automation or another safety-relevant engineered system.

• Experience with verification and validation test automation scenario generation requirements-based testing or performance benchmarking.

• Experience designing coverage measures scorecards confidence metrics or regression-detection methods.

• Experience with simulation-to-real-world correlation predictive-validity analysis or comparing results across test environments.

• Applied knowledge of probability statistics experimental design or data analysis including hypothesis testing confidence intervals power analysis sampling strategies or precision and recall.

• Experience with data pipelines SQL scientific computing or large-scale test execution.

• Graduate degree or equivalent depth in engineering physics applied mathematics statistics data science or a related field.

• Validation results have clear quantitative confidence and known limitations.

• Coverage metrics and sampling are tied to the claims and decisions they support.

• Regressions and progressions are detected reliably simulation performance is evaluated against real-world outcomes and the evidence supports decisions about risk readiness and release.

You are interested in how to know whether a complex system is working not only in how to implement one component. You enjoy moving between code data statistical models experiments and system-level questions and you are comfortable learning the physical and operational context needed to interpret results correctly.

You may come from systems engineering robotics physics mechanical or electrical engineering applied mathematics statistics data science or a related background. The common thread is the ability to program reason quantitatively about physical systems and apply both to the validation of a real system.

Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York Colorado California or Washington.
The salary range for this role: is $153200 to $234100. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance job level and individual performance.
Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical dental vision Health Savings Account Flexible Spending Accounts retirement savings plan sickness and accident benefits life insurance paid vacation & holidays tuition assistance programs employee assistance program GM vehicle discounts and more

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