Staff Machine Learning Engineer, ML Platform

March 19

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Woven by Toyota

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Mobility to Love, Safety to Live

software development • automated driving • vehicle software platform • smart city

1001 - 5000

Description

• 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

Requirements

• 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++