About Us

Agtonomy is a hybrid autonomy and tele-assist service platform that turns tractors and other equipment into autonomous machines. A robust sensor suite and custom software stack enable remote modes of operation with a higher margin of safety than conventional equipment. Agtonomy, through its OEM partners, will address both local agriculture skilled labor shortages and broader land maintenance operations, including wildfire prevention through land clearing. 

About the role

We are looking for a talented ML Infrastructure engineer to join the Autonomy team and help build state-of-the-art model training, evaluation, and deployment pipelines to aid in the development of the perception modules of our self-driving vehicles. You will be responsible for providing solutions to empower machine learning development and optimize offboard training.

What you’ll be doing:

• Design and implement machine learning tooling and workflows for analysis & augmentation of real-world data
• Develop and optimize training pipelines in distributed environments
• Establish automated ETL pipelines
• Programmatically increase training efficiency of different neural network architectures
• Improve the developer experience and performance of our scalable ML platform
• Develop application/ML metrics to measure model, perception system, and overall self-driving performance; analyze for performance optimization opportunities

• BS or above in Computer Science/Engineering and 4 years of industry experience in designing and implementing deep learning, ML, or data analytics infrastructure
• Experience working with production machine learning pipelines, from dataset collection and labeling to training and validation
• Knowledge in using common deep learning frameworks, e.g. Tensorflow, Pytorch, CaffeSkilled in C (11 or newer) and PythonDemonstrated experience in profiling CPU/GPU code
• Experience with developing, running, and managing container orchestration systems like Kubernetes
• Ability to thrive in a fast-moving, collaborative, small team environment with little supervision

• Experience working with data processing pipelines for training in the cloud (AWS, Azure, Google Cloud, etc.)Solid understanding of metrics, data analysis, and scientific evaluation
• Strong software engineering skills building well-designed, highly-maintained and high-reliability code used by other engineers
• Passion for sustainable energy and electric vehicle development