Senior Simulation Engineer - Machine Learning

Job description

[Woven by Toyota](https://woven.toyota/en) 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**The Automated Driving & ADAS organization focuses on developing a scalable, data-driven approach to autonomous and assisted driving software. We collaborate with partners across Toyota, offering numerous opportunities to integrate cutting-edge technology into consumer vehicles globally. To continuously improve and thoroughly test our driving systems, we build powerful developer workflows that leverage real-world data and state of the art in-house and third party simulation technologies. The simulation technologies we develop and integrate form the foundation of our virtual verification and validation efforts, enabling autonomy and ADAS stack development, regression testing, and model training. **WHO ARE WE LOOKING FOR?** The Simulation Core team is seeking an experienced Senior Simulation Engineer to lead the adoption and integration of advanced machine learning technologies, including world foundation models, into large‑scale simulation workflows. This role sits at the intersection of simulation and machine learning and will enable us to complement traditional simulation paradigms with workflows that enable large scale training and testing of machine learning-based autonomy stacks. By steering technical direction, collaborating with cross-functional teams, and mentoring developers, you will play a key role in evolving our critical AD/ADAS simulator tools. ### RESPONSIBILITIES - Lead adoption and integration of machine learning techniques in simulation subsystems and workflows (e.g. scenario creation, reactive agents and sensor/vehicle/intersection/world modeling). - Architect and extend our next generation core simulation technologies (e.g. agent frameworks, scenario engine, sensor simulation). - Partner with ADAS teams to improve the development and evaluation of ADAS software. - Help set the team roadmap to support the growing landscape of ADAS customers and use cases. - Demonstrate good design practices; alignment with stakeholders before, during, and after implementation is essential. ### MINIMUM QUALIFICATIONS - Bachelor's or Master's degree in Computer Science, Engineering, or a related field highly preferred. - 5+ years of relevant work experience. - Experience with machine learning frameworks such as PyTorch, Jax or Tensorflow (PyTorch preferred). - Experience in machine learning workflows, such as data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization - Strong programming skills in Python, Rust or C++. - Proven track record crafting well-designed, impactful solutions within allotted time and resource constraints. - Ability to think, design, and code for both the user’s and simulator developer’s perspectives. - An infectious self-driven passion for solving complex problems while learning new techniques and technologies! ### NICE TO HAVES - Familiarity with recent breakthroughs in machine learning (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures). - Experience with building real-time systems (a requirement of simulations for driver assistance and hardware in the loop scenarios). - Experience with build systems, continuous integration, and/or continuous deployment (CI/CD). - Experience in the automotive and/or self-driving autonomy domain.