JobsSenior Software Engineer, Motion Planning – DoorDash Labs
Senior Software Engineer, Motion Planning – DoorDash Labs
DoorDashSenior Software Engineer, Motion Planning – DoorDash Labs
DoorDashLocation
San Francisco, CA
Type
Full-time
Posted
6/7/2026
Compensation
$168,000 - $247,000 per year
Undergraduate with 5+ Years of Experience
Approval 98.3%·Filings 469·New hires 45·
✓ Established Sponsor
·FY 2025Job description
The Senior Planner Engineer role at DoorDash Labs focuses on developing automation and robotics solutions to enhance last-mile logistics. The team is composed of experienced professionals dedicated to creating technologies that improve efficiency for Dashers, merchants, and consumers. The ideal candidate will have a strong background in autonomy systems and will play a key role in solving critical challenges in motion planning. This position requires collaboration across various teams to ensure the delivery of robust and reliable autonomous systems.
Requirements
- You have a BS/MS/PhD in CS, EE, Robotics, or a related technical field, or equivalent practical experience.
- You have proficiency in using AI coding tools in the full software development lifecycle.
- You have strong C++ software engineering experience building robust, production-quality systems.
- You have experience developing or deploying motion planning, robotics, or real-time decision-making systems.
- You are comfortable working with data-driven evaluation, simulation, and performance metrics.
Responsibilities
- Design and ship real-time behavior and motion planning features for complex urban/suburb driving scenarios.
- Lead root-cause analysis and systematically eliminate planner-related disengagements and long-tail failures.
- Build scalable evaluation, simulation, and validation frameworks to ensure safe rollout to new geo-fenced regions.
- Collaborate cross-functionally with perception, prediction, controls, and platform teams to deliver end-to-end autonomy improvements.
- Contribute to architecture decisions that improve planner robustness, generalization, and computational efficiency.
Benefits
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