Senior Machine Learning Engineer, Frameworks

RESPONSIBILITIES

• Develop foundational ML components to improve speed and ease of development of advanced machine learning models specifically tailored for autonomous vehicles utilizing deep learning and large-scale data.
• Deploy extensible, scalable and efficient ML data curation, training and evaluation cloud pipelines.
• Analyze model performance metrics, model failure modes, statistical relevance of datasets, etc. to guide the overall ML engineering effort.
• Integrate modern technologies with rigorous safety standards while maintaining cost efficiency.
• Significantly contribute to the development of needed components for end-to-end ML training and deployment, from data strategy to optimization and validation.
• Operate cross-functionally and serve a dual hat role in identifying opportunities to improve production models while also trailblazing and generalizing involved methods and toolings to empower others.
• Be a champion of the scientific method and critical thinking in inventing state-of-the-art deep learning solutions.
• 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.
• 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

• BSc / BEng (MS / PhD nice-to-have) in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience.
• 3+ years of experience with Python, PyTorch/Tensorflow, and software engineering best practices.
• 2+ years of experience covering machine learning workflows, data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, and inference optimization.
• Comfortable in writing C++ code to help integrate with our autonomous vehicle platform.
• Deep understanding of runtime complexity, space complexity, distributed computing, and the application of these concepts in concrete, distributed ML training and evaluation.
• Experience working with temporal data and/or sequential modeling.
• Strong communication skills with the ability to communicate concepts clearly and precisely.

NICE TO HAVES

• Experience with deep learning approaches such as supervised/unsupervised learning, transfer learning, multi-task learning, and/or deep reinforcement learning.
• 2+ years of experience with Apache Spark, Airflow, Flyte, Flink, Ray, or similar ML pipelines technologies.
• Experience deploying and tuning ML models onto custom edge hardware in robotics applications.
• Previously worked at, or in close collaboration with ML Research Engineers to deliver a solid software foundation that other ML Engineers can build on.
• Proven track record of deploying ML models at scale in self-driving or related fields.
• Familiarity with production-level coding in time-limited task schedules.
• Experience in self-driving, robotics, computer vision, or motion planning.