Deep Learning Engineer
Posted 17 Apr 2026
Advertised as “Deep Learning Engineer ”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2–7 yrs
- Management level
- People management
- Employment
- On-Site
No salary listed for this role.
Field of studyComputer Science, Machine learning (or related)
Study levelBachelor's
Carbon Robotics is the leader in physical AI for agriculture, helping farmers become more profitable and sustainable. Its flagship product, the LaserWeeder™, is the world’s leading commercial laser weeding system with hundreds of units operated by farmers in 15 countries worldwide.
LaserWeeder combines computer vision, AI, robotics, and high-powered lasers to identify weeds and destroy them with sub-millimeter precision—no herbicides, hand labor, or soil disturbance required. Growers achieve weed control cost reductions of up to 80% and crop yield increases of 5–50%, with payback in one to three years. The system is powered by Carbon AI’s Large Plant Model (LPM), trained on 150 million labeled plants, and can start weeding any crop or field in minutes.
The company's second product is Carbon Autonomy, the most dependable tractor autonomy solution. Carbon Autonomy includes a retrofit tractor autonomy kit for John Deere tractors, autonomy software, and remote supervision for real-time interventions if required.
Carbon Robotics is based in Seattle, Washington State, USA and has raised $185 million in capital since 2018 from leading investors including NVIDIA’s NVentures and BOND.
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Deep Learning Engineer
As a Deep Learning Engineer at Carbon Robotics, you will contribute to designing, developing, and deploying novel deep learning systems that power our autonomous laser weeding robots in the field.
What You'll Do
- 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