Senior Machine Learning Engineer – Behavior Data

Senior Machine Learning Engineer – Behavior Data

Requirements

• 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.

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.

Preferred Qualifications

• 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).