Deep Learning Engineer

About this role

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

Knowledge, Skills & Abilities

• 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

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

• Experience mentoring engineers and contributing to team technical culture

Requirements

• 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)

In Office Requirements

• We're a collaborative, in-person team — this role is based in our Seattle office with at least 4 days per week on-site

Carbon Robotics follows equitable hiring practices. Flexibility in our hiring process allows hiring of talent at levels different from what are posted. The compensation range outlined is based on a target budgeted base salary. Individual base pay depends on various factors such as relevant experience and skill, Interview assessments and responsibility of role, job duties/requirements. Offers are determined…