Job Description

In this role, you will:

• Manage and grow a mixed team of software and machine learning engineers building simulation tools that are used broadly across engineering to validate autonomous vehicle safety and performance.
• Set and drive the team's technical direction, investing in agentic tooling to automate manual simulation workflows and deploying in-house generative models for exploratory vehicle testing.
• Lead high-visibility, cross-functional initiatives to scale the company's generative scenario creation capabilities, partnering with stakeholders across engineering and earning executive sponsorship. (note about reporting directly to VPs on some projects)
• Own delivery of simulation tooling and services, including scenario authoring tools (e.g., 3D visual editors) used broadly across engineering teams.
• Ensure simulation tooling keeps pace with new Operational Design Domain (ODD) capabilities and upcoming autonomy milestones.
• Recruit, mentor, and develop engineers across both software and ML disciplines, including hiring ML engineers; build a strong team culture and clear growth paths.
• Partner with product, safety, autonomy, and validation stakeholders to prioritize the roadmap, translate business needs into technical execution, and deliver against autonomy milestones.

Qualifications:

• 7+ years of software engineering experience, with demonstrated technical depth in simulation, developer tools, infrastructure, or a related domain.
• 2+ years directly managing software and/or ML engineering teams, including hiring, mentoring, and performance management.
• Experience partnering cross-functionally with leaders and stakeholders across engineering to prioritize and deliver a shared roadmap.
• Strong understanding of simulation, testing, or validation systems for complex, safety-critical software.
  Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Bonus Qualifications:

• Experience managing teams that combine traditional software engineering with applied ML or generative AI.
• Familiarity with generative models applied to simulation or testing (e.g., traffic-infilling, scenario creation, synthetic data).
• Experience building or scaling tools used broadly across a large engineering organization, such as visual editors and agentic authoring tools.
• Background in autonomous vehicles, robotics, or another safety-critical, sensor-driven domain.
  Track record of leading high-visibility initiatives with executive stakeholders.