Software Engineer, Agtonomy

• 5+ years of experience in software development for problems involving computer vision, machine learning, and robotic perception techniques
• Foundational understanding of deep learning: model layer design, loss function intuition, training best practices
• Experience handling large datasets efficiently and organizing them for training and evaluation
• Experience curating synthetic and real-world image datasets for training
• Strong proficiency in modern C++ and Python and experience writing efficient algorithms for resource-constrained embedded systems
• Ability to thrive in a fast-moving, collaborative, small team environment with lots of ownership
• Excellent analytical, communication, and documentation skills with demonstrated ability to collaborate with interdisciplinary stakeholders outside of Autonomy
• An eagerness to get your hands dirty by testing your code on real robots at real customer farms (gives “field testing” a whole new meaning!)

Desirable

• As a Perception / Machine Learning Engineer on the Autonomy Team, you will play a key role in solving challenging perception problems in outdoor vehicle automation
• Leveraging your experience, you will implement state-of-the-art ML perception techniques to improve how Agtonomy’s tractors perceive and understand the environments where they operate
• You will work closely with embedded, localization, and planning engineers on the team to design and evolve the upstream and downstream interfaces of the perception system
• This role is perfect for someone who loves implementing ML to tackle real world problems, and is excited about applying their experience to make robots perceive in rugged, agricultural environments
• Applying machine learning to solve challenging perception problems for autonomous systems (e.g. object detection, semantic segmentation, instance segmentation, dense depth, optical flow, tracking, etc.)
• Driving the architecture, deployment, and performance characterization of our deep learning models
• Refining and optimizing models for low-latency inference on embedded hardware
• Designing and building cloud-based training and labeling pipelines
• Collaborating with the hardware and embedded teams on sensor selection and vehicle packaging given safety requirements
• Writing performant, well-tested software, and improving code quality of the entire Autonomy team through code and design reviews

• 1. Phone Screen with People Operations (30 minutes)
• 2. Video Interview with the Hiring Manager (45 minutes)
• 3. Coding Challenge and Technical Challenge (1 hour with an Autonomy Engineer)
• 4. Panel Interview (Video interviews scheduled with key stakeholders, each interview will be 30 to 45 minutes)
• 5. Final Interviews (CEO and CFO 30 minutes each)