Staff Motion Planning Software Engineer

About Zipline

Zipline is the world’s largest and most experienced drone delivery service. We are on a mission to serve all humans equally by ensuring access to food, medicine and essential goods anytime, anywhere. We design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably. Today, Zipline operates on four continents, makes a delivery somewhere in the world every 30 seconds, and has completed millions of deliveries to date, including blood, vaccines, medical supplies, food, and retail products.

Our customers include the world’s largest and most prominent healthcare systems, governments, retailers, restaurants and global businesses who rely on us to save lives, reduce emissions, increase economic opportunity, and provide delivery from point A to point B as fast as possible. The drone is only 15% of what we’ve built to enable seamless, reliable, global operations.

Our system strengthens supply chains, reduces congestion, and gives people time back. With more than 140 million commercial autonomous miles safely flown, Zipline is redefining access to healthcare, consumer products, and food across the globe.

We operate at a global scale and are looking for practical problem solvers who thrive on real-world challenges and rapid growth. Our team is motivated by building systems that have a direct, meaningful impact on people’s lives and by scaling the future of logistics. We are seeking people who sculpt from first principles, enjoy facing adversity, and can do the impossible at record breaking speeds.

About You and The Role

Zipline operates the world’s largest autonomous logistics system (ground or air) — and in the coming year, we will be hyper-scaling from thousands to tens of thousands of carefully coordinated drone deliveries in several dense, dynamic U.S. metros. Our Autonomy Motion Planning team is looking for a Staff Software Engineer who is passionate about developing autonomous systems for the real world. This role will explore cutting-edge approaches to decision making and trajectory planning that will enable more accurate and timely deliveries, putting you in the position to make critical product decisions that will shape our future architectures.

What You'll Do

• Build real-time trajectory generation and decision-making for autonomous flight (search-based, sampling, MPC, convex/non-convex optimization).
• Tackle joint optimization across safety, energy, time, and reliability—balancing mission goals with vehicle constraints.
• Plan in uncertain environments with complex dynamics: robustness to wind/turbulence, degraded sensors, and partial observability.
• Design safety-critical software for avoiding obstacles and interfacing with humans while delivering and picking up packages
• Extend the autonomy stack for new aircraft and payloads; define clean interfaces with perception and controls.
• Prove it before flight: large-scale sim, SIL/HIL, log replay, and fault-injection.
• Mine real fleet data to validate safety metrics, improve models, and burn down long-tail failure modes.

What You'll Bring

• Master's degree in Computer Science or related field and 7+ years of experience building software for safety-critical systems (aerospace/AV/robotics).
• Strong in Rust/C++/C for real-time, fault-tolerant code on embedded/Linux.
• Depth in planning & search (e.g. A*/ anytime / RRT*/sampling), trajectory optimization/MPC—shipped on real robots/vehicles.
• Hands-on with simulation at scale, SIL/HIL, log replay, and metrics-driven validation.
• Evidence of shipping production-grade autonomy through ambiguous, noisy conditions—owning the last mile to reliability.
• Systems thinker who collaborates tightly with perception, controls, and flight ops; crisp docs and design reviews.

What Else You Need To Know

We value diversity at Zipline and welcome applications from those who are traditionally underrepresented in tech. If you like the sound of this position but are not sure if you are the perfect fit, please apply!

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