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.
TEAM Are you ready to make a significant impact at the forefront of automotive technology? At the Autolabeling team at Woven by Toyota, we tackle a wide range of exciting challenges, from optimization and 3D geometric computer vision to pioneering advancements in machine learning (ML) for perception autolabeling. Our mission is to automatically generate vast quantities of high-quality, consistent, and diverse autolabeled data, driving the evolution of our data-driven solutions.
Who We Are Looking For We're on the lookout for a passionate and talented Machine Learning Intern who is eager to contribute to developing our innovative systems. As an intern, you'll have the opportunity to collaborate closely with one of our Senior ML Engineers, embarking on a future-focused project. Your role will involve integrating cutting-edge ML architectures and techniques into our Autolabeling stack development, pushing the boundaries of our technology and enhancing our autolabeling capabilities.
This internship offers you the opportunity to work at the intersection of innovation and impact, where your contributions will play a crucial role in shaping the future of autonomous driving and intelligent systems. If you're driven by the desire to solve complex problems and leave a lasting mark on the industry, we invite you to join our team and become part of a group that's revolutionizing the way we see and interact with the world. Apply now and help us build the future of mobility.
RESPONSIBILITIES - Conduct research to integrate state-of-the-art ML components into our auto-labeling pipeline, enhancing its efficiency and accuracy.
- Prepare and manage datasets to support data-driven model development and validation.
- Engage in daily collaboration with a Senior ML Engineer, receiving mentorship and guidance while working on project tasks.
- Gain hands-on experience with production-level data and real-world applications, enhancing your practical skills in a professional setting.
- Summarize key findings and insights in a comprehensive final report, highlighting project outcomes and providing actionable recommendations.
MINIMUM QUALIFICATIONS - Currently pursuing a Masters or a PhD degree in Computer Science, Computer Engineering, Computer Systems, Data Science, or similar discipline
- Proven ability to develop ML models for image, video, 3D or 4D data processing and/or generation (must be supported by school projects and/or publications)
- Strong programming skills in Python and ML development with PyTorch
- Publications in top tier CV, ML or robotics conferences or journals
- Good written and verbal communication skills
- Passionate about self driving car technology and its potential for humanity
NICE TO HAVES (optional) - Experience developing ML models for autonomous driving or ADAS
- Experience developing Perception algorithms for autonomous driving or ADAS
The base pay for this position ranges from $30 - $55per hour.
This internship is a temporary, non-permanent position designed to provide practical experience and learning opportunities. The duration of the internship is 12 weeks. The total compensation offered to an intern will be dependent upon the individual's skills, experience, qualifications, location, and level.
Our Commitment • We are an equal opportunity employer and value diversity.
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