You will analyze real-world flight data to identify system performance issues and design experiments to improve the droid's motion planning and decision-making capabilities. You will also collaborate with cross-functional teams to deploy autonomy solutions that enhance the safety and reliability of drone deliveries.
Requirements overview
Candidates must be currently pursuing a Master's degree in CS, EE, Robotics, or a related field with familiarity in learning-based planning methods. You should possess strong software development skills, a statistical validation mindset, and the ability to work in person at the office.
Transportation, Logistics, Supply Chain and Storage
Employees
1,948
Type
Privately Held
Size
1,001-5,000 employees
Zipline was founded to create the first logistics system that serves all humans equally. Our aim is to solve the world’s most urgent and complex access challenges. Leveraging expertise in robotics and autonomy, Zipline designs, manufactures and operates the world’s largest automated delivery system. Zipline serves tens of millions of people around the world and is making good on the promise of building an equitable and more resilient global supply chain.
From powering Rwanda’s national blood delivery network and Ghana’s COVID-19 vaccine distribution, to providing on-demand home delivery for Walmart and enabling leading healthcare providers to bring care into the home in the United States, Zipline is transforming the way goods move. By transitioning to clean, electric, instant logistics, we can decarbonize delivery, decrease road congestion, and reduce fossil fuel consumption and air pollution, while providing equitable access for billions of people. The technology is complex but the idea is simple: a teleportation service that delivers what you need, when you need it. Zipline is inspiring people, governments, and businesses to imagine what is possible when goods can move as seamlessly as information.
To join the team, check out our career page: https://flyzipline.com/careers/
TechnologyEngineeringLogisticsSoftwareScience & Research
Description
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 the Droid Planning Team
The Droid Autonomy team owns the entire autonomy stack behind droid package pickup and delivery. This includes perception (understanding the world), high-level decision making (executing behaviors), and motion planning (navigating through obstacles to deliver packages safely and efficiently). It’s an extremely cross-functional team that brings together expertise in robotics, machine learning, planning, and systems engineering—working as one to make the droid a safe, reliable, and scalable real-world delivery system.
The Role
Work hands-on with an autonomous robot operating in the real world — to help expand its capabilities for safer and more reliable deliveries.
Analyze, validate real-world data to understand system performance and identify failure modes and come up with unique solutions.
Design experiments and models that improve the robot’s ability to make reliable decisions in highly variable, unpredictable environments.
Translate insights from validation into actionable improvements that directly impacts the speed, accuracy and safety of the robot delivery.
Collaborate closely with perception, autonomy, and flight testing teams to see how your work connects across the full robotics stack.
Gain experience with the end-to-end process of deploying autonomy at scale—from data collection and model development through validation and live operations
Be part of an extremely motivated team that is delivering real-world solutions at scale, not just research prototypes.
What You'll Do
Work with and iterate on a state-of-the-art autonomous system: analyze flight data, extract key features of the droid’s performance, and identify what it takes to improve motion planning in difficult and uncertain environments.
Apply data analysis, statistics, and machine learning techniques to increase the reliability and robustness of the droid’s deliveries.
Design and run validation experiments to measure success and failure rates under different real-world conditions (e.g., driveway geometry, car presence, time of day).
Develop tools and pipelines to evaluate motion planning models, quantify uncertainty, and connect improvements directly to field performance.
What You'll Bring
Currently pursuing a Master’s in CS, EE, Robotics, or a related field with a familiarity with learning-based planning methods.
Software Proficiency and the ability to write safe and performant code; willing to learn new languages and adapt.
Statistical reasoning & validation mindset: able to design experiments, account for bias/variance, quantify confidence, and interpret results to guide decisions.
Structured problem solver: can break ambiguous, real-world problems into hypotheses and executable plans; comfortable making and testing assumptions.
Able to work in person at our office and thrive in a collaborative, hands-on environment.
What Else You Should Know
The final cash pay for this role will depend on a variety of factors, including a specific candidate's experience, qualifications, skills, and working location. The total compensation package for each role may also include: a housing stipend; overtime pay; relocation support; paid sick time; and more.
Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws or our own sensibilities.
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!
Zipline is also committed to providing reasonable accommodations to individuals with disabilities. Please let your point of contact at Zipline know if you require any accommodations throughout your interview process.
Voluntary Self-Identification
For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiring process or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.
As set forth in Zipline ’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.