About Zipline
Do you want to change the world? Zipline is on a mission to transform the way goods move. Our aim is to solve the world’s most urgent and complex access challenges by building, manufacturing and operating the first instant delivery and logistics system that serves all humans equally, wherever they are. From powering Rwanda’s national blood delivery network and Ghana’s COVID-19 vaccine distribution, to providing on-demand home delivery for Walmart, to enabling healthcare providers to bring care directly to U.S. homes, we are transforming the way things move for businesses, governments and consumers. The technology is complex but the idea is simple: a teleportation service that delivers what you need, when you need it. Through our technology that includes robotics and autonomy, we are decarbonizing delivery, decreasing road congestion, and reducing fossil fuel consumption and air pollution, while providing equitable access to billions of people and building a more resilient global supply chain.
Join Zipline and help us to make good on our promise to build an equitable and more resilient global supply chain for billions of people.
About You and The Role
In service of our mission to operate at global scale, we’re growing our perception capabilities, to expand quickly and safely into new products and locations, with the ultimate goal of delivering essential packages right to your doorstep. Our perception team is looking for a software engineer with expertise in machine learning and artificial intelligence utilizing sensor data and a passion for developing and shipping perception systems for the real world.
What You'll Do
- Design integrated machine learning solutions that enable our highly maneuverable aircraft to perceive general aviation aircraft, power lines, radio towers, and other challenging obstacles, so that we can steer clear of, dodge or otherwise avoid them
- Build software infrastructure to enable learning algorithms to leverage our large-scale fleet data
- Contribute to state-of-the-art machine learning infrastructure and relevant software (e.g. distributed training, continuous model integration, data management, and evaluation of production systems).
- Address large scale challenges in the machine learning development cycle, especially around distributed training in the cloud, and data engineering.
- Stay up to date on the state-of-the-art in deep learning ideas and software
- Implement cutting-edge deep learning models accelerating model training time, improving performance, and tackling open problems
- Understand the inner workings of neural networks to uncover edge cases and make safety determinations
- Design machine learning systems that can understand and communicate when they are not working well.
- Identify and mitigate bottlenecks in our machine learning development processes
What You'll Bring
- 5+ years of professional experience developing software for hardware products in a safety-critical field, e.g. aerospace, robotics, medical devices, autonomous vehicles
- Deep understanding of the theory and practice of modern machine learning techniques
- Deep learning expertise: Experience training deep-learning models in an end-to-end fashion, writing custom layers/operations, optimizing networks for inference on embedded systems.
- Clear grasp on basic linear algebra, optimization, statistics, and algorithms.
- Experience working with Pytorch, Tensorflow or other modern deep learning frameworks.
- You are passionate about ML, both large scale engineering and research challenges, especially in the space of autonomous driving and/or robotics.
- Strong software engineering practices in Python with machine learning experience in a production setting.
- Computer vision experience not required, but recommended.
- Generalist mindset, with the ability to work cross platform, from spooling up cloud compute services to optimizing for embedded systems
- Experience building reproducible data and machine learning pipelines
- Experience deploying machine learning solutions on real robots is a big bonus
What Else You Need to Know
The starting cash range for this role is $180,000 - $225,000. 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, qualifications, skills, working location, and projected impact. The total compensation package for this role may also include: equity compensation; overtime pay; discretionary annual or performance bonuses; sales incentives; benefits such as medical, dental and vision insurance; paid time off; and more.
Zipline is an equal opportunity employer and prohibits discrimination and harassment of any type without regard to race, color, ancestry, national origin, religion or religious creed, mental or physical disability, medical condition, genetic information, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity, gender expression, age, marital status, military or veteran status, citizenship, or other characteristics protected by state, federal or local law or our other policies.
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!
Top Skills
What We Do
Zipline is the world's largest autonomous delivery network and is powered entirely by fixed-wing drones. Our fleet circles the equivalent distance of the equator every 2.5 days, and we have shipped hundreds of thousands of critical medical products across Rwanda, Ghana, and now beginning in the United States.
Why Work With Us
Zipline is the perfect intersection of super cutting-edge tech, deep social mission, and extremely compelling business case. Our small, scrappy, customer-obsessed, humble, and mission-driven team has set the bar for what is possible in the drone logistics industry globally, and has designed some incredibly elegant technology in the process.
Gallery
