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About the Role
As an ML Training & Inference Infrastructure Engineer on the Data Platform team, you will build and scale systems powering Zipline's data flywheel. This role operates at the intersection of autonomy and infrastructure, owning systems that enhance ML development speed, reproducibility, observability, and safety. You will work across the full ML development cycle, from data ingestion to model deployment, with a focus on production engineering and improving autonomous systems.
Responsibilities
- Build and operate software infrastructure for learning algorithms to leverage Zipline’s fleet data.
- Design scalable, maintainable data and ML infrastructure for autonomy teams, covering dataset creation, validation, training, evaluation, and deployment.
- Own and improve data pipelines feeding the ML development loop.
- Identify and mitigate bottlenecks in the ML development cycle, focusing on orchestration, performance, and reproducibility to enhance the delivery experience.
Requirements
- 3+ years of professional software engineering experience, ideally including ML infrastructure, data infrastructure, robotics, autonomy, aerospace, medical devices, or another safety-critical hardware/product environment.
- Strong software engineering practices in Python in a production setting, including designing APIs, services, schemas, jobs, and operational workflows.
- Experience building reproducible data pipelines and machine-learning pipelines.
- Experience monitoring data statistics, system performance metrics, pipeline failures, and model/evaluation signals.
- Working knowledge of ML concepts such as datasets, training, evaluation, optimization, statistics, and modern deep learning workflows.
- Generalist mindset and willingness to work across cloud services, data platforms, developer tooling, and embedded/robotics-adjacent constraints.
- Strong ownership, clear communication, and interest in building secure systems for mission-critical workflows.
Skills
- Python
- PyTorch or similar ML frameworks
- Kubernetes or other container orchestration systems
- Cloud and on-premise production infrastructure (preferably AWS)
- Infrastructure-as-code tools (e.g., Terraform, CloudFormation)
Location
- Remote
Work Type
- Full-time
Experience Level
- ML Infrastructure Engineer II
- Senior ML Infrastructure Engineer
- Staff ML Infrastructure Engineer
Salary/Compensations
- $160,000 - $250,000
Benefits
- Equity compensation
- Discretionary annual or performance bonuses
- Sales incentives
- Medical, dental and vision insurance
- Paid time off
About the Company
- Zipline is the world’s largest and most experienced drone delivery service, aiming to serve all humans equally by providing access to essential goods anytime, anywhere.
- They design, build, and operate the world’s largest autonomous logistics system, delivering critical supplies quickly and reliably.
- Zipline operates on four continents, making a delivery every 30 seconds and has completed millions of deliveries, including medical supplies, food, and retail products.
- Their customers include major healthcare systems, governments, retailers, and global businesses.
- The Zipline system strengthens supply chains, reduces congestion, and has safely flown over 140 million commercial autonomous miles.
- They are looking for practical problem solvers motivated by building systems with meaningful impact and scaling the future of logistics.
Equal Opportunity
- 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.
- They value diversity and welcome applications from those traditionally underrepresented in tech.
- Completion of the voluntary self-identification survey is entirely voluntary and will not affect the hiring process.