Autonomy Engineer - ML & DL Infrastructure
Builds and scales ML/DL infrastructure including data pipelines, annotation workflows, training, deployment, and monitoring for autonomous drone systems. Requires hands-on experience in data engineering, cloud ML platforms, containerization, and MLOps.
About the job
How You’ll Make an Impact
- Design and implement scalable, extensible, interactive data pipelines and annotation workflows
- Build tools that leverage state-of-the-art machine learning systems for efficient data exploration and curation across the fleet of Skydio drones
- Design and implement pipelines for data ingestion, versioning, model training, deployment and monitoring
- Optimize and scale deep learning training workflows to improve team iteration velocity
- Leverage your expertise and best-practices to uphold and improve Skydio’s engineering standards
What Makes You a Good Fit
- Demonstrated hands-on experience with data engineering and building large scale, performant and efficient data processing pipelines
- Demonstrated hands-on experience with cloud-based ML platforms, containerization technologies, ML Ops platforms and databases
- Experience and understanding of security and compliance requirements in ML infrastructure
- Demonstrated hands-on experience building and managing ML pipelines including data preparation, model training, model deployment and monitoring
- Demonstrated ability to take a concept and systematically drive it through the software lifecycle: architecture, development, testing, and deployment, and monitoring
- Comfortable navigating and delivering within a complex codebase
- Strong communication skills and the ability to collaborate effectively at all levels of technical depth
Compensation
- Annual base salary range: $170,000 - $277,500
- Equity in the form of stock options
- Comprehensive benefits: group health insurance, paid vacation, sick leave, holiday pay, 401K savings plan
- Relocation assistance may be provided
Skills
Machine Learning, Deep Learning, Data Pipelines, Ml Ops, Kubernetes, Docker, Cloud Platforms, Data Engineering, Model Training, Model Deployment, Data Ingestion, Data Versioning, Monitoring, Containerization, Databases
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