Senior Computer Vision/Machine Learning Engineer, Digital Twin Platform

About Woven by Toyota Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society.

Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.

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Toyota is redefining what it means to move . We're challenging the current state of mobility by enhancing the movement of people, goods, information and energy. Centered around three core concepts - A Living Laboratory™, Human-Centered, and Ever Evolving City™ - Woven City serves as a test course for mobility to fulfill our purpose of well-being for all. We do this by bringing together a diverse community of people with a shared passion for the future of mobility to co-create, develop and refine innovative products and services. This cross-section of social infrastructure, mobility, and people provides a unique opportunity for inventors, residents and visitors to interact seamlessly with new technologies throughout daily life in an environment that emulates a real city. Our team, Digital Twin Platform, as one of the core Social Infrastructure/Common Platform teams under the Woven City umbrella, is responsible for the realization of Woven City's vision: "Expand mobility. Enhance humanity. Engage society" by developing a robust software platform, on one hand to enable inventors and business owners to “test” their products in a virtual environment before deploying to the physical Woven City; on the other hand, to create analytics to assist in decision making based on both simulated results and more importantly operational data collected from the physical city, such as residents daily life, traffic dynamics, energy consumption, etc. As the Digital Twin Platform is still in its early growth phase, you'll have the rare chance to influence not just what we build but how we bring it to market and experiment with different business models as the platform and its commercial strategy mature together. For more information about Woven City, please visit: https://www.woven-city.global/

We’re looking for a Senior Computer Vision and Machine Learning Engineer who can go beyond integrating pre-built APIs and off-the-shelf pipelines. You’ll design, train, evaluate, and optimize models at a deep technical level, applying them to real-world challenges across logistics hubs, distribution centers, and factories. As a Senior Engineer, you’ll be expected to lead technical direction, define standards, and operate with a high degree of autonomy. You will shape how the team approaches hard problems, influence architecture across the CV/ML stack, and help grow the engineers around you. Your work may include detecting and tracking workers, vehicles, and packages; analyzing space utilization; and interpreting worker activity to identify productive workflows and operational inefficiencies. We value candidates with a strong research background, including PhD-level expertise in a relevant field. However, a PhD is not required, we also welcome engineers with substantial applied experience and a comparable level of technical rigor. You’ll join a small, high-ownership team where your technical judgment will directly shape both the product’s direction and its real-world impact.

Design, train, and optimize deep learning models for object detection, multi-object tracking, classification, and activity recognition in industrial environments Build and maintain real-time computer vision inference pipelines and analytics systems deployed across logistics, distribution, and factory operations Lead dataset strategy, annotation standards, model evaluation, and experimentation to continuously improve CV/ML performance and reliability Define and drive the technical roadmap, architecture, and integration strategy for CV/ML systems within the broader platform ecosystem Establish engineering best practices and mentor engineers through technical leadership, design reviews, and adoption of state-of-the-art CV/ML advances

At least a bachelor’s degree in a relevant field and 7+ years of hands-on CV/ML engineering experience, or a PhD in a relevant field plus 5+ years of applied experience Hands-on experience in model design, training, and hyperparameter tuning, with strong proficiency in PyTorch or TensorFlow Experience leading the development of scalable perception pipelines, including state-of-the-art object detection and multi-object tracking systems, with strong understanding of model trade-offs, data quality strategy, evaluation methodology, and real-world deployment constraints Proven track record of delivering complex CV/ML systems end-to-end in production environments Demonstrated ability to lead technical projects, drive architectural decisions, and influence engineering direction Solid understanding of classical computer vision geometry - camera intrinsics/extrinsics, calibration, and 3D reconstruction fundamentals Strong English communication skills - written and verbal

PhD in Computer Vision, Machine Learning, or a closely related field Experience with human pose estimation, skeleton-based action recognition, or temporal activity recognition in video Familiarity with edge inference optimization: TensorRT, ONNX export, quantization, or pruning Experience with 3D sensing modalities such as LiDAR, depth cameras, or multi-camera calibration and fusion Background working in industrial, logistics, or warehouse environments Experience with space utilization analysis, occupancy estimation, or pedestrian/vehicle flow analysis Hands-on experience building video annotation pipelines or active learning loops Publications in top-tier CV/ML conferences (e.g., CVPR, ECCV, ICCV, NeurIPS, or similar) Experience with Vision-Language Models (VLMs) or agentic system design Hands-on experience with cloud platforms (AWS, GCP) for ML workloads

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