Software Engineer - ML/Computer Vision (Battery Sorting)

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About the role

About Redwood Materials Redwood Materials was founded in 2017 by JB Straubel, co-founder and former CTO of Tesla, on a simple but radical idea: the critical minerals powering the energy transition already exist. They just need to come back. Today Redwood is the largest lithium-ion battery recycler in North America — recovering, refining, and redeploying critical minerals from end-of-life batteries back into the supply chain. On the energy side, Redwood has deployed the largest second-life battery project in the world, delivering power to AI data centers in months rather than years using proprietary energy architecture engineered entirely in-house. The company is profitable, the market it operates in is projected to grow 20 to 50 times over the next decade, and Redwood is the only company with the supply chain, the technology, and the operational scale to lead it. Software Engineer, ML/Computer Vision (Battery Sorting) The Battery Sorting team at Redwood Materials is building a world-class, ML-enabled sorting platform that uses computer vision and machine learning to classify and route thousands of end-of-life batteries per hour across diverse chemistries and form factors. This role sits at the intersection of software engineering and machine learning, with direct ownership of the production systems powering automated battery sorting on the factory floor. The ideal candidate is equally comfortable debugging a production incident as iterating on a model, and will have the opportunity to generate patents in automated battery classification. This is a high-impact, highly visible role with immediate real-world application in advancing the energy transition. Hours Full-time | Schedule may vary depending on site operational needs; flexibility required Responsibilities will include: • Develop, test, and maintain production software systems powering automated battery sorting, spanning ML inference, image acquisition, sensor integration, and hardware-adjacent control interfaces • Train and deploy computer vision models for battery chemistry classification, including dataset annotation, preprocessing, and evaluation within established data pipelines • Build and maintain services and APIs that connect ML outputs to downstream systems including MES, HMI, and PLC/controls interfaces • Own observability across the production software stack through structured logging, metrics dashboards, alerting, and on-call triage for inference pipelines and supporting services • Monitor model performance in production to catch regressions or distribution shifts and drive iterative improvements through data analysis and retraining • Contribute to infrastructure-as-code and CI/CD workflows to validate, version, and deploy application code and ML model artifacts to production environments • Collaborate cross-functionally with Controls, Hardware, Manufacturing, DevOps, and IT teams to translate operational needs into software and model improvements Desired Qualifications: • B.S. in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience • 2+ years of industry experience working with machine learning models, preferably in computer vision • Hands-on experience with ML frameworks and libraries such as PyTorch and OpenCV • Experience contributing to production codebases and pipelines with an emphasis on clean, well-documented, and well-tested code • Experience designing and tracking ML experiments using tools such as MLflow • Familiarity with edge deployment or model optimization techniques for inference (e.g., quantization, TensorRT, ONNX Runtime) in latency-sensitive or resource-constrained environments • Experience with OCR, image classification pipelines, or multi-sensor and multimodal fusion • Experie

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