Senior Data Scientist, Autonomous Vehicle Machine Learning en Woven-by-toyota

TEAMAt Woven by Toyota, we tackle Autonomy challenges at the intersection of AI, Robotics, and Advanced Driving. Our work includes a diverse array of challenges and activities, such as analyzing petabytes of multimodal driving data, solving optimization problems in computer vision, minimizing latency on hardware accelerators, deploying scalable and efficient machine learning (ML) training and evaluation pipelines, and designing novel neural network architectures to advance state-of-the-art ML for Perception, Prediction, and Motion Planning. We are looking for doers and creative problem solvers to join us in improving mobility for everyone with human-centered automated driving solutions for personal and commercial applications.WHO ARE WE LOOKING FOR?The team is looking for a Senior Data Scientist to spearhead the creation and deployment of sophisticated data models and algorithms to tackle business challenges for the Autonomy team. You will enhance and streamline scalable data pipelines, automation systems for machine learning, and performance assessment. You will also develop algorithms, pipelines and key strategies to optimize the utilization of large-scale fleet data for our next-generation automated driving solutions, influencing millions of Toyota customer vehicles. We are looking for individuals who are passionate about self-driving car technology and its potential impact on humanity.

RESPONSIBILITIES

Lead the development of complex data models and algorithms to solve business problems for the Autonomy team.

Design and implement data strategies for collecting, sampling, labeling, and using large scale datasets to enhance machine learning model performance.

Develop metrics and tools for anomaly detection and trend analysis in data from various sources, including real-world vehicle platforms and simulations.

Analyze model performance metrics, model failure modes, statistical relevance of datasets, etc. to guide the overall ML engineering effort.

Design and improve scalable data pipelines and automation for machine learning and performance evaluation.

Prepare and present detailed reports and visualizations to stakeholders, clearly communicating complex results to both technical and non-technical audiences.

Stay current with advancements in data science and machine learning technologies, driving the adoption of best practices.

Work in a high-velocity environment and employ agile development practices.

Exhibit a "Giver" mindset, proactively asking, “What can I do for you?” to facilitate production development processes while maintaining a "get things done" mentality.

Collaborate closely with teams such as Perception, Motion Planning, Simulation, Infrastructure, and Tooling to drive unified solutions.

Work in a hybrid workspace, with the requirement to be present in our Nihonbashi (Japan), Palo Alto (California), or Ann Arbor (Michigan) offices three days per week.

MINIMUM QUALIFICATIONS

MS or PhD in a quantitative field (e.g. Statistics, Mathematics, Physics)

3+ years of experience solving large-scale data science problems

2+ years of experience with Python and SQL data analysis libraries and packages

Experience with theoretical aspects of data science and machine learning (deep learning, statistical analysis, and mathematical modeling)

Experience in building machine learning algorithms and infrastructure, such as: data pre- and post-processing, sampling and curation, ablation studies, evaluation

Strong communication skills with the ability to communicate concepts clearly and precisely.

NICE-TO-HAVES

Conducting thorough analyses of large-scale multimodal driving data

Leveraging statistical techniques and machine learning algorithms to derive data insights

Build or manage infrastructure, such as Docker, Kubernetes, Jenkins, GitHub Actions

Experience with temporal/sequential and/or spatial data

Experience with computer vision (e.g.multi-view geometry, camera calibration, depth estimation, neural radiance fields, gaussian splatting, simultaneous localization and mapping) 

Experience with robot motion planning (e.g., trajectory optimization, sampling-based planning, model predictive control) 

Experience in self-driving challenges (Perception, Prediction, Mapping, Localization, Planning, Simulation).

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