ML Infrastructure Engineer
About this role
The Data Platform team at Zipline builds and scales the software infrastructure that powers an autonomous drone delivery network spanning four continents. Operating at the intersection of autonomy and infrastructure, this engineer owns systems designed to make machine learning development faster, reproducible, observable, and safe. Daily work involves managing data ingestion, processing pipelines, dataset creation, distributed training, continuous model integration, evaluation, and deployment across large-scale fleet data.
Created to support the continuous improvement of real-world autonomous systems, the role requires strong production engineering habits and a generalist mindset. Candidates who enjoy working across cloud services, data platforms, developer tooling, and robotics-adjacent constraints will find a natural home here. A successful individual thrives on practical problem-solving and aims to build robust infrastructure that directly impacts mission-critical delivery operations.
What you will do
- Build and operate software infrastructure enabling learning algorithms to leverage large-scale fleet data.
- Design scalable and maintainable data and machine learning infrastructure for autonomy teams, covering dataset creation, validation, training, evaluation, and deployment.
- Own and improve data pipelines that feed directly into the machine learning development loop.
- Identify and mitigate bottlenecks in the machine learning development cycle with a focus on orchestration, performance, and reproducibility.
- Increase the rate of delivery experience improvement and scaling through optimized developer tooling and operational workflows.
What you need
- Bring three or more years of professional software engineering experience, ideally within machine learning infrastructure, data infrastructure, robotics, autonomy, aerospace, medical devices, or safety-critical hardware environments.
- Demonstrate strong software engineering practices in Python within production settings, including comfort designing APIs, services, schemas, jobs, and workflows.
- Possess experience building reproducible data pipelines and machine-learning pipelines.
- Monitor data statistics, system performance metrics, pipeline failures, and model or evaluation signals.
- Apply working knowledge of machine learning concepts such as datasets, training, evaluation, optimization, statistics, and modern deep learning workflows.
- Utilize experience with Kubernetes or other container orchestration systems for production workloads, alongside cloud and on-premise infrastructure like AWS and Terraform.
Nice to have
- Experience deploying or evaluating ML systems on real robots, autonomous vehicles, drones, or other hardware products.
- Experience with large-scale training systems, feature stores, data and versioned artifact stores, model registries, or experiment tracking.
- Experience with annotation systems, dataset inspection tooling, or active-learning workflows.
Benefits
- Equity compensation
- Discretionary annual or performance bonuses
- Medical, dental, and vision insurance
- Paid time off
Skills and tech stack
Questions candidates ask
What is the salary range for this role?
The starting cash range is $160,000 to $250,000. Final cash pay depends on factors including experience, qualifications, skills, working location, and projected impact.
What technologies and tools will I use?
The tech stack includes Python, PyTorch, Kubernetes, AWS, and infrastructure-as-code tools such as Terraform or CloudFormation. You will work across cloud services, data platforms, and developer tooling.
What kind of background is preferred for this position?
Candidates should have three or more years of professional software engineering experience, ideally in machine learning infrastructure, data infrastructure, robotics, autonomy, aerospace, medical devices, or another safety-critical hardware or product environment.