Deep Learning Engineer

Carbon Robotics Carbon Robotics · Robotics · Carbon Robotics, Corporate · Deep Learning

Deep Learning Engineer to design, develop, and deploy novel deep learning systems for autonomous laser weeding robots. This role involves leading experiments for computer vision in agricultural environments, owning model optimization and deployment pipelines, driving end-to-end ML workflows, and partnering with Engineering and Product Management. Requires experience in production computer vision, deep learning frameworks (PyTorch), C++, and taking ML projects from inception to business impact.

What you'd actually do

  1. Lead the design and execution of experiments to develop and validate novel deep learning architectures for computer vision in agricultural environments
  2. Own model optimization and deployment pipelines — ensuring high performance, reliability, and scalability across operational field deployments
  3. Drive end-to-end ML workflows from data strategy and pipeline design through evaluation and production deployment
  4. Define best practices for experimentation, documentation, and model evaluation within the team
  5. Partner with Engineering and Product Management to scope, prioritize, and deliver high-impact features

Skills

Required

  • 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 mentoring engineers and contributing to team technical culture
  • 2-7 years of experience in deep learning model optimization and deployment
  • BS+ in Computer Science, Machine Learning, or a related field (or equivalent experience)

Nice to have

  • 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
  • Strong verbal and written communication skills — able to explain complex model behavior and tradeoffs to non-technical staff and customers

What the JD emphasized

  • novel deep learning architectures
  • production computer vision systems
  • deep learning mathematics
  • first-principles thinking
  • ML projects from inception through business impact
  • deployment at scale
  • real-time inference

Other signals

  • autonomous laser weeding robots
  • computer vision
  • deep learning
  • production computer vision systems
  • ML workflows
  • deployment at scale