Machine Learning Engineer

Skills

Use statistical and machine learning techniques to create scalable solutions for vehicle telemetry data and video analysis and perform R&D to drive discovery of new generation mobility products Establish scalable efficient automated processes for large scale data analysis model development model validation and model implementation Drive adoption of best practices across organizations Deliver production-ready code Work with Product Owners to define the KPIs for machine learning projects Stay abreast of developments in research methodology and changing technologies in the marketplace and proactively identify applications of these latest developments to improve existing methods Prepare and present findings to both technical and non-technical audiences Work within the constraints of time budget and resources capacities to align with Toyota’s global vision Develop and foster collaborative relationships with product business and engineering teams to effectively serve our customer needs

Description

Toyota Connected’s Mobility team is responsible for building and managing our connected vehicle platforms, supporting product research using vehicle sensor data and creating new and exciting data services for Toyota customers that makes driving safer, more convenient, and fun. We’re looking for a Sr Machine Learning Engineer capable of using machine learning and statistical techniques to create state-of-the-art solutions for non-trivial, and arguably, unsolved problems. If you are results driven, interested in how to apply advanced machine learning techniques, would love to work with vehicle telemetry data and video, are deeply technical, highly innovative, and long for the opportunity to build solutions for challenging problems that directly impact the company's bottom-line, we want to talk to you.

Mid Data Scientists will prototype, design, and implement predictive models and algorithms. They will lead the full life cycle including data cleansing, feature creation, and iterative model selection. They will write maintainable and extensible tested code that adheres to standards, produce specifications, and determine operation feasibility, and troubleshoot, debut, and upgrade existing systems.

Responsibilities

• 5+ years of production experience working in Data Science or Software Engineering
• 3+ years of production experience in Machine Learning, Computer Vision, Signal Processing, and/or Natural Language Processing (NLP)
• Solid production experience using Python (including NumPy) and SQL
• Solid production experience using TensorFlow and/or PyTorch
• Production experience with Apache Spark
• Strong fundamentals in problem solving, algorithm design and complexity analysis
• Experience implementing and orchestrating Machine Learning pipelines in production environments, using tools such as Kubeflow, airflow, Pachyderm, mlflow, etc.
• Hands-on experience with web APIs, containers, Kubernetes, CI/CD and testing
• Experience from working in Agile Scrum environments
• Experience implementing solutions in a cloud environment (AWS, Azure, or Google Cloud)
• Experience using Infrastructure-as-code for cloud infrastructure automation
• Experience working with data science in automotive telematics data and video is a plus
• Experience in edge computing is a plus