Autonomy Engineer - ML & DL Infrastructure
Build and scale the infrastructure, data pipelines, and MLOps workflows supporting deep-learning training and deployment for autonomous drones. The role requires hands-on data engineering, cloud ML platforms, containerization, databases, and end-to-end software lifecycle experience.
About the job
Responsibilities
- Design and implement scalable, extensible, interactive data pipelines and annotation workflows.
- Build tools leveraging state-of-the-art machine learning systems for efficient data exploration and curation across a drone fleet.
- 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.
- Apply engineering expertise and best practices to uphold and improve engineering standards.
Requirements
- Hands-on experience with data engineering and building large-scale, performant, efficient data-processing pipelines.
- Hands-on experience with cloud-based machine learning platforms, containerization technologies, MLOps platforms, and databases.
- Experience and understanding of security and compliance requirements in ML infrastructure.
- Hands-on experience building and managing ML pipelines, including data preparation, model training, model deployment, and monitoring.
- Ability to drive concepts through the software lifecycle: architecture, development, testing, deployment, and monitoring.
- Comfort navigating and delivering within a complex codebase.
- Strong communication skills and ability to collaborate effectively across technical levels.
Skills
Data Engineering, Data Pipelines, Machine Learning, Deep Learning, MLOps, Cloud Computing, Containerization, Databases, Data Ingestion, Model Training, Model Deployment, Model Monitoring, Python, Security Compliance, Software Architecture
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