Software Engineer, Image Processing and Machine Learning

About Redwood Materials

Redwood Materials was founded in 2017 to create a circular supply chain for electric vehicles and clean energy products, making them more sustainable and driving down the cost for batteries. We’re doing this by developing and deploying new technologies to increase the scope and scale of recycled and sustainable materials in the global battery supply chain.

Software Engineer, Image Processing & Machine Learning

Essential Duties:

We are seeking a Software Engineer with expertise in image processing, machine learning, and AI-driven classification to support the development of automated material identification and sorting systems. This role will focus on developing algorithms to process images, train models for classification, and integrate intelligent automation solutions into Redwood’s recycling and material recovery workflows. The ideal candidate has a strong background in computer vision, deep learning, and software engineering, with a passion for sustainability and high-impact innovation. 

If necessary, we will adjust the job level to suit your experience and responsibilities. 

 Responsibilities will include:  

• Develop and optimize image processing algorithms for analyzing high-resolution images of materials across multiple angles and spectra (visual, X-ray) 

• Train machine learning models for classification and object detection optimized for material sorting and recycling processes.

• Work with X-ray, hyperspectral, and other imaging modalities to extract relevant features for automated decision-making.

• Integrate computer vision and ML models into Redwood’s software infrastructure for real-time material classification and automated control systems.

• Process and analyze large datasets to improve model accuracy, optimize inference speeds, and reduce false positives.

• Collaborate with software, robotics, and hardware teams to develop scalable, production-ready solutions.

• Deploy, monitor, and maintain ML pipelines, ensuring reliability and robustness in an industrial setting.

• Optimize neural network architectures for efficient inference on edge devices and embedded systems.

• Work with cloud-based or on-premises computing environments to handle large-scale training and inference workloads.

• Continuously explore new techniques in AI, deep learning, and computer vision to drive innovation and improve efficiency. 

 Desired Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Electrical Engineering, or a related field.

• 3+ years of experience in image processing, computer vision, or machine learning applications.

• Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch, OpenCV).

• Strong understanding of convolutional neural networks (CNNs), object detection, and classification techniques.

• Experience with image datasets, preprocessing techniques, and feature extraction.

• Familiarity with hardware acceleration for AI inference (e.g., TensorRT, ONNX, CUDA, Edge TPU).

• Experience deploying machine learning models in real-world environments (cloud, edge computing, or embedded systems).

• Ability to process large datasets and optimize model performance for real-time applications.

• Experience with X-ray or hyperspectral imaging is a plus.

• Familiarity with industrial automation, robotics, or manufacturing environments is a plus. 

Physical Requirements:  

• Ability to perform the essential job functions consistent safely and successfully with the ADA, FMLA and other federal, state and local standards, including meeting qualitative and/or quantitative productivity standards.

• Ability to maintain regular, punctual attendance consistent with the ADA, FMLA and other federal, state, and local standards 

Working Conditions:  

• Environment, such as office or outdoors.

• Ability to work in challenging working conditions which may include exposure to noise, dust, chemicals, and temperature extremes, while protected by PPE, for extended periods of time. 

• Essential physical requirements, such as climbing, standing, stooping, or typing. 

• Occasional work weekends, nights, or be on-call as a regular part of the job. 

• Occasional travel requirements.

The position is full-time. Compensation will be commensurate with experience.

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