Deep Learning Engineer

The Carbon Robotics LaserWeeder™ leverages advanced robotics, computer vision, AI/deep learning, and lasers to eliminate weeds with sub-millimeter accuracy—all without herbicides. This innovative solution reduces environmental impact, promotes soil health, and helps farmers address labor shortages and rising costs. Designed in Seattle and built at our cutting-edge manufacturing facility in Richland, Washington, the LaserWeeder is setting a new standard for automated weed control. With $157 million in funding from prominent investors such as BOND, NVentures (NVIDIA’s venture arm), Anthos Capital, Fuse Venture Capital, Ignition Partners, Revolution, Sozo Ventures, and Voyager Capital , Carbon Robotics is driving innovation. As a no-nonsense team with a bias for action, we take pride in executing our ideas. Whether it’s designing transformative technology or visiting farms to ensure our products are reliable and safe, we do whatever it takes to deliver for our customers.  Working here means tackling big problems with big impact. You’ll find opportunities to grow professionally, solve complex challenges, and make meaningful contributions to a mission that matters. At Carbon Robotics, we trust our team to act independently and make practical, real-world decisions.  Join us as we innovate, execute, and build the future of farming together. YouTube | X | Instagram | LinkedIn | News 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 using our equitable hiring practices. Carbon Robotics offers additional compensation in the form of benefits premiums, pre-IPO stock options and On Target Earning commissions for appropriate positions. Base pay ranges are reviewed each year. We are committed to the principle of pay equity – paying employees equitably for similar