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Staff Engineer Team Lead, Simulation Infrastructure - Release

Motional
CompanyMotional
CategoryEngineering
LocationLas Vegas
RemoteOn-site (inferred)
EmploymentNot stated
LevelManager
SalaryNot stated by the employer
Posted24 Jul 2026
Last verified4 Aug 2026
SourceEmployer ATS (greenhouse)
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Description
Mission Summary: Motional is at the forefront of the driverless revolution. We are seeking a Staff Engineer Team Lead to grow and lead our Release team, specializing in Software Release and Validation. In this critical role, you will be the technical and team leader responsible for designing, building, and deploying the infrastructure that ensures safe and reliable software releases across our autonomous vehicle fleet. You will play a vital part in realizing product improvements by managing the complex intersection of software builds, triage, and large-scale simulation-based validation. The ideal candidate has a deep technical background in both release engineering and autonomous vehicle technologies, combined with a proven track record of leading and mentoring engineering teams. You should be able to work independently, taking ownership of large, vaguely defined problems while working cross-functionally across the entire organization. Strategic Impact: Release Strategy & Architecture: Drive the organizational strategy for release management. Evaluate and optimize branching paradigms (e.g., Trunk-Based vs. Gitflow) to balance developer velocity with release stability. Divergence Resolution: Audit and resolve discrepancies between mainline and active release branches. Enforce a "Mainline First" policy to reduce technical debt and manual cherry-picking. Continuous Improvement: Develop and iterate on robust, gated testing to ensure main-branch stability, preventing the need for complex, fragmented branching models. What You'll Be Doing: Release Governance & Strategic Architecture: Define, enforce code and execute promotion policies, branching strategies (e.g., Trunk-Based vs. Gitflow), and release lifecycles to ensure release isolation and stability. Shift-Left Quality Leadership: Drive shift-left quality initiatives by specifying and generating automated tests across all pipeline stages, ensuring quality is integrated throughout the release lifecycle rather than retrofitted. Release Process Standardization: Audit, unify, and standardize release documentation and governance workflows across the organization to eliminate tribal knowledge and empower the broader engineering organization. Release Validation & Gating: Oversee the evolution and content in all release pipelines stages to minimize branch breakages and increase release quality. Improve visibility into release metrics, ensuring stakeholders and leadership have the necessary data for rapid, informed decision-making. Cross-Functional Integration: Collaborate with Autonomy teams to seamlessly and safely integrate necessary fast-forward patches into release branches. Proactively manage and mitigate technical risks associated with software deployment and build compatibility. What We're Looking For: Experience: 5+ years of professional experience in the autonomous driving or robotics industry, specifically within release engineering or simulation. Autonomy Expertise: Strong understanding of autonomy systems, including Localization, Perception, Prediction, Planning, Control, and Autonomy Platforms. Technical Leadership: Experience working on and leading large-scale cross-functional projects and managing engineering teams. Engineering Foundation: BS or MS in Engineering or equivalent knowledge and work experience. Programming Proficiency: Proficiency with C++/Python and advanced experience with CI/CD pipelines. Problem-Solving Skills: Demonstrated ability to think logically, remove bias from problem evaluation, and data mine for insights on deficiencies and overlooked optimizations. Collaboration: Strong teamwork, communication, presentation and writing skills for working with developers, program managers, and leadership. Strategic Problem Solving: Proven ability to navigate complex engineering debates (e.g., Trunk-Based vs. Gitflow), provide data-driven recommendations, an