Woven by Toyota is the mobility technology subsidiary of Toyota Motor Corporation. Our mission is to deliver safe, intelligent, human-centered mobility for all. Through our Arene mobility software platform, safety-first automated driving technology and Toyota Woven City — our test course for advanced mobility — we’re bringing greater freedom, safety and happiness to people and society. 
Our unique global culture weaves modern Silicon Valley innovation and time-tested Japanese quality craftsmanship. We leverage these complementary strengths to amplify the capabilities of drivers, foster happiness, and elevate well-being.
TEAMWe work in ML training and deployment ecosystem, you will be embedded within the Automated Driving & ADAS team and work directly with Autonomy ML engineers to accelerate development and deployment of ML models while improving their performance in joint projects. Your work will directly improve quality of our production models deployed on cars and serve as a basis to develop tools to support training and deployment of ML models on large scale data. You will work with a set of large-scale and interconnected tools, and help in improving scalability, efficiency, and utility of all aspects of the ML ops ecosystem. You will operate across ML Platform, production model owners, research, and cloud infrastructure to deliver efficient fast tools for integration, testing, and deployment of those models. This role is hybrid and will report to the Engineering Manager located in Palo Alto, CA.

RESPONSIBILITIES:

• Develop and integrate SOTA methods for efficient, large-scale training of ML models and support multi-platform deployment including automotive-grade edge compute devices
• Operate cross-functionally and serve a dual hat role to identify opportunities to improve production models, for example by improving model architectures and while trailblazing and generalizing involved methods and toolings to empower others
• Architect and develop tools for ML model evaluation and end-to-end validation to help ML engineers assess impact of their changes to downstream customers and modules
• Improve the utility of our vehicle data by building and improving the infrastructure for data sampling, data curation and data representation; improve versatility of our ML data format to support data reuse across models
• Operate cross-functionally and identify bottlenecks such as latency hot spots during training and deployment of ML models, while generalizing needed tools to empower others
• Scale our ML Ops architecture to the next level while taking advantage of heterogeneous clusters to maximize resource efficiency

QUALIFICATIONS:

• ML/ML Ops engineer with extensive experience in building large-scale data intensive distributed applications
• Experience in the full ML Ops cycle covering data cleansing, data sampling, data curation, training, testing, and deployment in the cloud and on edge compute platforms
• Prior experience of building and shipping ML models in a production environment
• Knowledge of SOTA methods in ML infrastructure domain and demonstrated curiosity and track record for keeping up with the literature
• Expert Python and PyTorch practitioner
• Familiarity with deployment and tuning ML models for edge devices
• Familiarity with C++

161,000 - 264,500 a yearYour base salary is one part of your total compensation. We offer a base salary, short term and long term incentives, and a comprehensive benefits package. The California pay scale for this full time position is $161,000 - 264,500. The total compensation offered to an employee will be dependent upon the individual's skills, experience, qualifications, location, and level.
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