Deep Learning Engineer

Job description

Job responsibilities

• Lead the design and execution of experiments to develop and validate novel deep learning architectures for computer vision in agricultural environments
• Own model optimization and deployment pipelines — ensuring high performance, reliability, and scalability across operational field deployments
• Drive end-to-end ML workflows from data strategy and pipeline design through evaluation and production deployment
• Define best practices for experimentation, documentation, and model evaluation within the team
• Partner with Engineering and Product Management to scope, prioritize, and deliver high-impact features
• Mentor and provide technical guidance to mid-level and junior engineers
• Communicate model architecture decisions, tradeoffs, and performance results to both technical and non-technical audiences

Job requirements

• 2-4 years of professional experience designing and implementing novel deep learning architectures for production computer vision systems
• Deep understanding of foundational deep learning mathematics and the ability to apply first-principles thinking to architecture decisions
• Hands-on experience working across the software stack, including sensor integration and web services, ideally within a robotics or autonomous field equipment platform
• Experience with deep learning frameworks, particularly PyTorch, and proficiency in C++ for performance-critical model development and deployment
• Proven track record taking ML projects from inception through business impact — including data strategy, pipeline development, experimentation, and deployment at scale
• Strong expertise in modern object detection techniques (vision transformers, anchor-free detectors, embeddings, and beyond)
• Experience in autonomous driving or ADAS is a plus — background in perception pipelines, sensor fusion, or real-time inference in outdoor or unstructured environments is highly valued
• Comfort navigating ambiguity and making principled technical decisions in rapidly evolving technical landscapes