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Principal Engineer, Autonomy

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Outsiders Fund
Full Time position
Listed on 2026-10-18
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Software Architect, Full Stack Developer, Software Engineer
Salary/Wage Range or Industry Benchmark: 300000 - 420000 USD Yearly USD 300000.00 420000.00 YEAR
Job Description & How to Apply Below

Who We Are

Aero Vect is transforming ground handling with autonomy, redefining how airlines and ground service providers around the globe run day-to-day operations. We are a Series A company backed by top-tier venture capital investors in aviation and autonomous driving. Our customers include some of the world’s largest airlines and ground handling providers. For more information, visit

We are hiring a Principal Engineer for Autonomy — the senior-most individual contributor in our autonomy organization. You will have the deepest hands-on technical ownership of one or more of Perception, Prediction, and Planning, with cross-stack influence across the rest of the autonomy stack. You will report directly to the VP of Engineering, with no direct reports of your own, but with the expectation that you set technical direction the rest of the autonomy team follows.

We are looking for someone who is excellent both at the systems level and at execution — a senior IC with the technical depth to anchor the hardest decisions in autonomy and the bias for shipping to convert those decisions into running code on a vehicle.

You Will

You are the senior-most IC in autonomy, with the deepest technical ownership of either Perception, Prediction, or Planning (or any combination thereof) and influence across the rest of the stack.

  • Perception. Own the design and evolution of the perception stack — detection, classification, tracking, and multi-modal sensor fusion across the available modalities. Drive perception robustness across the long tail of real-world operating conditions, and set the direction for where and how deep learning is best applied across the perception pipeline.

  • Prediction. Own the prediction stack and the design of models for intent inference, behavior forecasting, and handling occlusions and edge cases. Set the direction for how prediction integrates with perception upstream and planning downstream.

  • Planning. Own the design of the planning and decision-making stack, from structured driving behaviors to the domain-specific maneuvers required for autonomous GSE operations. Set the direction for where learned components earn their place in the planner.

  • Cross-stack influence. Set the technical direction at the interfaces between your primary areas and the rest of the stack, and partner with the other senior engineers in autonomy to keep the system coherent end-to-end.

  • Autonomy architecture
    . Own the functional and SW architecture of the autonomy stack, and partner with neighboring teams towards its implementation.

You Have
  • 15+ years of hands-on experience building production autonomy systems, with strong technical depth across multiple modules (localization, perception, prediction, planning, controls). You think at the level of the autonomy system, not a single module.

  • Demonstrated track record of shipping autonomy components that have run in production on real vehicles at non-trivial scale — not just research prototypes or simulation results.

  • Prior experience as the most senior individual contributor in an autonomy organization — setting direction, mentoring staff/senior engineers, and partnering with engineering leadership without managing a team yourself.

  • Deepest technical depth in perception, prediction, or planning (ideally more than one of the three).

  • Strong software engineering fundamentals in C++ and Python. You write or review code that other senior engineers want to extend and trust in a safety-relevant system.

  • Fluency with modern deep learning for autonomy, including the practical realities of training, evaluation, deployment, and lifecycle management of models that have to work in the real world.

  • Experience working in or with ROS / ROS 2 and the distributed-systems realities of on-vehicle…

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