Data Engineer for AI-Driven Decarbonization Analytics

Toyota Research Institute; Los Altos (CA); Presencial; USD 144.000 - 207.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Medical, dental, vision insurance

401(k) eligibility

Paid time off

Annual cash bonus

Descripción de la vacante

Toyota Research Institute (TRI) is seeking a Data Engineer to design and build data systems powering analytics, forecasting, and AI-driven research within the CNS team. You will shape data flows from diverse sources to reliable datasets used in predictive modeling and LLM-enabled workflows, balancing engineering rigor with research flexibility.

You will partner with ML researchers, technical leads, and scientists to translate evolving needs into robust data architectures, collaborating with

Formación

• 3–5 years of data/ software engineering with a data focus.

Responsabilidades

• Design, implement, and maintain data pipelines ingesting structured, semi-structured, and unstructured data.
• Develop data models to support predictive modeling and LLM workflows.
• Improve data tooling and automation for rapid prototyping.
• Integrate third-party APIs and global data sources.
• Establish data quality, lineage, governance, and reproducibility standards.
• Participate in exploratory data analysis to validate assumptions.
• Collaborate with internal and external partners on secure data access and governance.

Conocimientos

Python

SQL

Unix tooling

Git workflows

Data modeling

Fast experimentation

Communication

Educación

Bachelor's degree in quantitative field

Herramientas

AWS

Databricks

ETL frameworks

Descripción del empleo

Toyota Research Institute (TRI) is seeking a Data Engineer to design and build data systems powering analytics, forecasting, and AI-driven research within the CNS team. You will shape data flows from diverse sources to reliable datasets used in predictive modeling and LLM-enabled workflows, balancing engineering rigor with research flexibility.

You will partner with ML researchers, technical leads, and scientists to translate evolving needs into robust data architectures, collaborating with