Autonomy Droid Perception SWE - Onboard Systems

Zipline Senior And Staff Perception Engineer

Zipline is operating the world's largest autonomous logistics network—delivering critical medical and commercial goods globally with high reliability, precision, and scale. As we expand into increasingly complex, safety-critical environments, the ML systems behind our autonomy stack must be robust, adaptable, and deeply integrated with the hardware.

We're hiring senior and staff perception engineers to join our Droid team, the group responsible for the autonomy that powers Zipline's backyard delivery experience. You'll build realtime 3D perception ML models that transform multiple camera inputs into scene geometry and semantics that help our mission planner with delivery and package pickup. You'll develop across the entire perception stack, from optimizing the onboard TensorRT engines to building a data flywheel that finds interesting samples from our long-tail of customer deliveries.

You will work closely with the planner team to make sure we build the right system, rather than just the best perception model.

This is not a research role—you'll be expected to move fast, ship production-grade systems, and find clever ways to apply state-of-the-art techniques to tangible, high-impact problems.

• Implement, train and evaluate real-time 3D perception models that work with two or more cameras across one or more timesteps
• Deploy and run these models onboard a resource-constrained computer, finding ways to optimize and reduce compute and memory footprints
• Build visualization, introspection and eval tooling to deeply understand model performance both on test datasets as well as "in the wild"
• Work closely with the motion planning team, building an expressive yet compact interface between the two subsystems and tracking the right metrics to ensure we're always hill-climbing towards a better overall system
• Zipline moves fast. On average, every 6 weeks, you will ship a new feature (or sometimes even a new model!) to production.
• At the Senior level, you'll lead architectural decisions, drive experimentation, and own outcomes for a particular model head or backbone. At the Staff level, you will own road mapping the future of one or more onboard models and manage cross-functional interfaces in addition to the above.
• Stay up to date with research in the field, drive experimentation, go to conferences and help keep Zipline's ML modeling stack in lockstep with powerful new paradigms in real-time compute-constrained 3D perception

• At least 5+ years of experience (Senior) or 8+ years of experience (Staff) building and deploying deep learning-based perception systems, particularly in 3D geometry, semantic segmentation, or learned multi-view stereo
• Strong understanding of classical computer vision (e.g. camera calibration, epipolar geometry, structure-from-motion, SGBM stereo) and the ability to blend it with modern ML approaches.
• Expertise and depth with robotics fundamentals: you should be able to reason about reference frames, matrix math, SE(3) manifolds and probabilistic sensor fusion
• Hands-on experience training, iterating on, and optimizing CNN and transformer architectures on target hardware: think NVIDIA Jetson sized compute
• An engineering mindset focused on outcomes over experimentation—you know how to prioritize what's good enough to ship now and what needs to be architected for scale later.
• At the Senior level, you bring experience with the full ML lifecycle (training, data annotation, evaluation, hard mining)—not just models. At the Staff level, experience influencing design across systems: data pipelines, training infrastructure, compute requirements.
• ML experience on hardware applications is a strong plus - a robot will move based on the outputs of your perception system

The starting cash range for this role is $200,000 - $240,000 for Senior and $240,000 - $280,000 for Staff. Please note that this is a target, starting cash range for a candidate who meets the minimum qualifications for this role. The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience,…