What You’ll Do
Level up how ML teams work with data
Develop automation and tooling that give ML engineers deep insight into labeling workflows and data quality (e.g., efficiency dashboards, auto‑QA, autolabel review tools), reducing iteration time from idea to trained model.
Apply ML to labeling itself
Collaborate with ML engineers to design and integrate ML‑driven data annotation (pre‑labeling, autolabeling, active learning loops), helping us move from human‑only to machine‑led labeling at scale.
Build high‑impact labeling experiences
Design, implement, and test scalable, high‑performance user experiences and services using modern full‑stack and/or frontend technologies. You’ll ship features spanning multiple surface-areas that directly affect how quickly and accurately we can label data for new models and cities.
Champion AI‑assisted engineering
Use and advocate for modern AI‑powered development workflows (code assistants, automated documentation, test generation, etc.) to increase build-velocity while maintaining code and product quality.
Basic Qualifications
Recently completed a bachelor’s, master’s, or PhD degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, Machine Learning, or a related STEM field. For completed degrees, graduation must have occurred within the past 12 months.
Experience shipping software or features through internships, research, academic projects, or prior professional work.
Programming experience in one or more languages such as Python, TypeScript, JavaScript, Go, Java, or C++.
Familiarity with software fundamentals, including object-oriented design, design patterns, data structures, algorithms, API/interface design, and engineering best practices.
Strong communication and collaboration skills; you can explain tradeoffs, influence peers, and work through ambiguity with cross‑functional partners.
Interest in autonomous vehicles, robotics, machine learning, data-centric AI, or developer and ML platform technologies.
Preferred Qualifications
Degree completed between May 2025 and August 2026, with availability to begin employment in 2026.
Hands-on experience leveraging AI tools (agentic workflows, knowledge acquisition, documentation generation, operational triage, etc) to accelerate understanding, implementation, debugging, and delivery of new capabilities.
Proficiency in writing and reviewing high‑quality, scalable, and performant full-stack code using technologies and languages like Python, TypeScript, Go, React, SQL, Redux, gRPC, GraphQL, WebGL, etc.
Solid understanding of scalable software system design including data modeling and API/interface design.
Strong fundamentals in object‑oriented design and design patterns, data structures, algorithms, and engineering best practices (TDD, code quality, observability, CI/CD).
Driven to learn new technologies and deepen your expertise across frontend, backend, and data/ML‑adjacent systems.
Empathetic to user challenges (from labelers to ML engineers to Ops) and excited to turn messy workflows into simple, intuitive tools.
Location
Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to our Suynnvale, CA office three times per week, at minimum.
This job may be eligible for relocation benefits
Compensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.
The salary range for this role is $125,000 to $165,000. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
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Benefits Overview
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