Machine Learning Engineer, End-to-end Autonomy (London)
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Woven by Toyota is enabling Toyotas 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, were working toward one bold goal: a world with zero accidents and enhanced well-being for all. Team At Woven by Toyota, we are at the forefront of developing advanced Machine Learning solutions for autonomous driving. Our team tackles groundbreaking challenges in designing state-of-the-art neural networks, pioneering innovative end-to-end architectures, and advancing ML techniques in perception, prediction, and motion planning. We're passionate about pushing the boundaries of autonomous systems through deep learning and optimization, particularly in complex 3D geometric computer vision scenarios. We're seeking passionate innovators and creative problem-solvers eager to redefine mobility through cutting-edge AI and robotics, contributing directly to shaping the future of self-driving technology. Woven by Toyota is developing a joint project between Toyota Research Institute (TRI) and Woven by Toyota to research and develop a fully end-to-end learned automated driving / ADAS stack. This cross-org collaborative project is synergistic with TRIs robotics divisions efforts in Diffusion Policy and Large Behavior Models (LBM). Responsibilities- Support the design and development of ML models or model components for end-to-end autonomous driving: ranging from initial data strategy, design, development, experimentation, evaluation, and deployment;
- Able to navigate ambiguities and address uncertainties arising from complex projects involving multiple teams and legacy codebases;
- Writes high-quality code while being rigorous with Machine Learning experimentation;
- Collaborate closely with stakeholders from multiple teams in different time zones to define interfaces and requirements for an end-to-end stack;
- MS, or higher degree, in a related field, or equivalent industry experience
- Professional experience with ML frameworks such as PyTorch, Jax or Tensorflow (PyTorch preferred)
- Experience with data sampling and data curation pipelines for autonomous driving datasets
- Experience in state-of-the-art architectures for end-to-end autonomous driving
- Experience in ML workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization
- Python and C++ experience
- Experience with infrastructure for large-scale datasets and distributed model training
- Experience working with a modern cloud service (AWS, GCP, Azure etc.)
- Hands-on experience with autonomous driving systems
- Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling)
- Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures)
- Knowledge of autonomous driving, large-scale data curation pipelines
Seniority level
Seniority level
Entry level
Employment type
Employment type
Full-time
Job function
Job function
Engineering and Information TechnologyIndustries
Software Development
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#J-18808-Ljbffr- Location:
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