Full-Stack ML Data Platform Engineer for Autonomous AVs

Stack AV; United States; On-site; USD 130,000 - 190,000; Full time

Job summary

Stack AV is building a leading AI-driven data infrastructure for autonomous vehicle development in the United States. The role focuses on ingesting, processing, curating, annotating, serving and visualizing AV data to empower ML and inference across engineering teams.

You will collaborate with product and engineering to implement scalable tools, improve data workflows, and drive impactful data-driven decisions while maintaining a strong emphasis on safety and reliability.

Qualifications

• 3+ years of experience driving full-stack development with large-scale diverse data.
• Experience building modern full-stack solutions using React, TypeScript, Go, Python for many customer teams.
• Solid understanding of data storage architectures and SQL/NoSQL.
• Proven track record of scalable, reliable full-stack solutions in fast-paced environments.
• Ability to articulate design trade-offs and gain alignment.
• Experience integrating novel AI techniques into front-end applications.

Responsibilities

• Partner with cross-functional stakeholders to define a vision for a state-of-the-art AV data introspection software and a pragmatic implementation roadmap.
• Deliver the roadmap while maintaining high engineering standards.
• Identify issues in existing tools and address gaps.
• Lead technical discussions to deliver timely solutions.
• Advocate technical solutions across the company to maximize business impact.

Skills

Full-stack development

React

TypeScript

Go

Python

SQL

NoSQL

System architecture

Design trade-offs

AI integration

Job description

Stack AV is building a leading AI-driven data infrastructure for autonomous vehicle development in the United States. The role focuses on ingesting, processing, curating, annotating, serving and visualizing AV data to empower ML and inference across engineering teams.

You will collaborate with product and engineering to implement scalable tools, improve data workflows, and drive impactful data-driven decisions while maintaining a strong emphasis on safety and reliability.