Embedded Software Engineer (Data Platform, Autonomy)

Requirements Master's degree in Computer Science or related field and 3+ years of experience building software for safety-critical systems (aerospace/AV/robotics/medical) Strong in Rust/C++/C for real-time, fault-tolerant code on embedded/Linux Depth in planning & search (A*/anytime/RRT*/sampling), trajectory optimization/MPC, or multi-agent deconfliction—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 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! What the job involves 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 Senior/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 Build real-time trajectory generation and decision-making for autonomous flight (search-based, sampling, MPC, convex/non-convex optimization) Design collision avoidance and large-scale multi-agent planning (fleet deconfliction, airspace rules, traffic management) that scales to 10,000+ flights/day Tackle joint optimization across safety, energy, time, and reliability—balancing mission goals with vehicle/airspace constraints Plan in uncertain environments with complex dynamics: chance-constraints, robustness to wind/turbulence, degraded sensors, and partial observability Extend the autonomy stack for new aircraft and payloads; define clean interfaces with perception and controls Prove it before flight: scenario libraries, SIL/HIL, large-scale sim, log replay, and fault-injection Mine real fleet data to validate safety metrics (separation, risk, intervention rate), improve models, and burn down long-tail failure modes Continuously reduce cost/latency (vectorization, warm starts, smart heuristics) while raising reliability #J-18808-Ljbffr Zipline