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SeniorВ офисеПолная занятость

Senior or Staff MLE - Droid Perception (Onboard)

Оценка ИИ

Исключительная вакансия в компании-лидере рынка автономной доставки с прозрачной вилкой зарплаты и высокой социальной значимостью продукта. Работа над сложнейшими инженерными задачами на стыке софта и железа с реальным применением в мировом масштабе.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует редкого сочетания глубоких знаний в 3D-геометрии, классическом компьютерном зрении и современных нейросетевых архитектурах, а также навыков оптимизации под ограниченное железо (TensorRT, Jetson). Высокая ответственность за безопасность полетов и необходимость работы с 'длинным хвостом' реальных данных делают позицию крайне сложной.

Анализ зарплаты

Медиана220 000 $
Рынок170 000 $ – 280 000 $
Оценка ИИ

Предложенная вилка $180k - $265k полностью соответствует рыночным стандартам для позиций Senior/Staff MLE в Кремниевой долине. Верхняя граница диапазона является очень конкурентоспособной даже для топовых технологических компаний США.

Сопроводительное письмо

I am writing to express my strong interest in the Senior/Staff MLE position for the Droid Perception team at Zipline. With over 5 years of experience in deploying deep learning models for autonomous systems, I have a proven track record of bridging the gap between complex 3D perception research and resource-constrained hardware. My background in classical computer vision, combined with expertise in optimizing Transformer and CNN architectures for NVIDIA Jetson-class devices, aligns perfectly with your mission to enable precise backyard deliveries.

What excites me most about Zipline is the direct real-world impact of your technology. I am particularly drawn to the challenge of building a robust data flywheel and optimizing TensorRT engines to handle the long-tail of customer delivery environments. I am an engineer who prioritizes production-grade outcomes and thrives in environments where perception systems must operate with high reliability in safety-critical scenarios. I look forward to the possibility of contributing to the world's largest autonomous logistics network.

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Присоединяйтесь к Zipline, чтобы создавать системы компьютерного зрения, которые спасают жизни и меняют логистику будущего уже сегодня!

Описание вакансии

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 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 systems behind our autonomy stack must be robust, adaptable, and deeply integrated—especially at the intersection of perception and deployment.

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. This team owns the full stack of onboard, offboard and cloud-side perception systems enabling precise and reliable delivery in complex customer backyards. You’ll build realtime 3D perception models that capture geometry, scene semantics and preference for both 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.

What You’ll Do

  • Implement, train and evaluate real-time 3D perception models that work with two or more cameras across one or more timesteps
  • 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”
  • Help design and implement data selection pipelines that identify the most valuable data from the field, then help our annotation teams label these faster through the use of prelabeling or pseudo-ground-truthing these samples.
  • Work closely with the droid planner team, building a strong interface between the two subsystems and tracking the right metrics to ensure we’re always hill-climbing towards a better overall system
  • Stay up to date with research in the field, drive experimentation, and help keep Zipline’s modeling stack in lockstep with powerful new paradigms in real-time compute-constrained 3D perception

What You'll Bring

  • At least 5+ years of experience building and deploying deep learning-based perception systems, particularly in 3D geometry, semantic understanding, or mapping from cameras
  • 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.
  • Familiarity with building training, data annotation, and evaluation pipelines—not just models.
  • Comfort working across systems: jumping into data pipelines, training infrastructure, or debugging distributed training issues as needed.
  • Experience deploying models in real-world, high-stakes robotics or autonomy applications is a strong plus  - a robot will move based on the outputs of your perception system

Why This Team?

  • You’ll own novel real-world autonomy problems that matter—delivering essential goods around the world, in thousands of diverse backyards and environments.
  • Your work will be used thousands of times a day by real end-customers receiving their deliveries by drone in production
  • You’ll have autonomy and trust to define the roadmap and drive architectural decisions that shape Zipline’s global operations.
  • You’ll work on cutting-edge problems with people who care deeply about quality, systems thinking, and solving hard problems with elegance and clarity.

A Few More Things

Zipline is an equal opportunity employer and encourages candidates from historically underrepresented backgrounds to apply—even if you're not sure you're a perfect fit. If you care about shipping high-impact autonomy systems in production, we want to hear from you.

What Else You Need to Know

The starting cash range for this role is $180,000 - $265,000. 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!

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Навыки

  • Computer Vision
  • Deep Learning
  • 3D Geometry
  • TensorRT
  • NVIDIA Jetson
  • CNN
  • Transformers
  • Python
  • PyTorch
  • TensorFlow
  • Sensor Fusion
  • Robotics
  • C++

Возможные вопросы на собеседовании

Проверка фундаментальных знаний в робототехнике и понимания систем координат, необходимых для 3D-восприятия.

Как вы обрабатываете неопределенность при преобразовании координат из системы камеры в мировую систему координат при условии зашумленных данных о положении дрона?

Оценка практического опыта оптимизации моделей для работы в реальном времени на бортовом оборудовании.

Опишите ваш опыт оптимизации Transformer-архитектур для NVIDIA Jetson. Какие техники квантования или прунинга вы использовали и как это повлияло на точность?

Проверка умения работать с данными и строить процессы обучения (data flywheel).

Как бы вы спроектировали систему автоматического отбора наиболее ценных кадров из миллионов часов полетов для дообучения модели детекции препятствий?

Оценка навыков в классическом CV, которые необходимы для дополнения DL-подходов.

В каких случаях вы предпочтете использовать SGBM или классическую стереометрию вместо глубокого обучения для оценки глубины, и как их можно эффективно комбинировать?

Проверка инженерного мышления и умения работать в команде с разработчиками планирования траекторий.

Как вы определяете метрики успеха для системы восприятия, чтобы они напрямую коррелировали с качеством работы планировщика (planner), а не просто показывали mAP модели?

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flyzipline
Страна
США
Зарплата
180 000 $ – 265 000 $