Software Engineer - ML/Computer Vision (Battery Sorting)

Redwood Materials

McCarran, California

Full Time

Posted June 23, 2026

$153k - $200k

Energy Storage

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Job Description

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.

Key Responsibilities

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

Requirements

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
Experience working in or alongside industrial, manufacturing, or operations environments where software interacts with physical systems
Strong cross-functional communication skills and ability to prioritize and execute in a fast-paced, dynamic environment
A passion for sustainability and making the world a better place!