Senior ML Systems Engineer

Description

Help teach self-driving vehicles to see and understand the world.

The Data Labeling Engineering team builds and runs tools and pipelines that combine human and machine labeling to train the autonomous vehicle machine learning models in the company's AV organization.

The work sits where software engineering, data engineering, and AI/ML meet. The team sets the strategy, tooling, and quality controls behind reliable training data at foundation-model scale, and thousands of users rely on its tools.

The stack includes TypeScript, React, Python, GraphQL, Golang, and ML model services for data annotation pipelines. You will work closely with AI/ML engineers, Product Operations, Product Management, Data Science, and other ML Platform groups.

The role suits an engineer who wants end-to-end ownership of major parts of the platform, a path toward technical leadership, and a direct effect on the next generation of AV capabilities.

Responsibilities

• Build and test scalable, high-performance user experiences and services across full-stack or frontend work, shipping features on several surfaces that determine how fast and how accurately new models and cities get labeled.
• Create automation and tooling, such as efficiency dashboards, auto-QA, and autolabel review tools, that show ML engineers how labeling works and how good the data is, so they can move from idea to trained model faster.
• Work with ML engineers to design and integrate machine-driven annotation, including pre-labeling, autolabeling, and active learning loops, to shift labeling from human-only to machine-led at scale.
• Use and promote AI-assisted workflows like code assistants, generated documentation, and test generation to speed up delivery without lowering quality.
• Own projects from problem framing through design, build, and rollout, and lead code reviews, design discussions, and technical decisions.
• Turn vague requirements from ML, Ops, Product, Data Science, and platform partners into workflows, APIs, and UIs that meet quality, cost, and latency targets.

Requirements

• 6+ years of experience building robust distributed platforms and applications.
• Hands-on use of AI tools, such as agentic coding, search, and documentation generators, to speed up understanding, implementation, debugging, and delivery.
• Proficiency writing and reviewing high-quality, scalable, performant full-stack code in Python, TypeScript, Go, React, SQL, Redux, GraphQL, and WebGL.
• Solid knowledge of relational databases, data modeling, and API design.
• Strong grounding in object-oriented design, design patterns, data structures, algorithms, and engineering practices such as TDD, code quality, observability, and CI/CD.
• Experience developing and operating cloud-based applications.
• Passion for self-driving technology and its potential to improve safety, mobility, and driving.
• Drive to keep learning new technologies and deepen your skills across frontend, backend, and data/ML-adjacent systems.
• Proven record of shipping and operating end-to-end products or features in production.
• Clear communication and collaboration. You can explain tradeoffs, influence peers, and make progress with cross-functional partners when requirements are unclear.
• Empathy for users, from labelers to ML engineers to Ops, and an interest in turning messy workflows into simple, intuitive tools.
• Competitive edge: experience using modern web APIs such as Service Workers, Cache Storage, and IndexedDB in data-intensive or visualization-heavy applications.
• Competitive edge: a track record of working closely with customers, product managers, designers, and user experience researchers.
• Competitive edge: experience with computer vision, machine learning, or data-centric AI, especially where labeled data, data quality, or autolabeling loops were central.
• Competitive edge: familiarity with labeling platforms used by large labeling workforces, such as annotation UIs, workflow engines, and quality systems.
• Competitive edge: experience with A/B testing and telemetry or observability systems to measure impact and reliability.
• Competitive edge: proficiency writing and reviewing high-quality, scalable, performant code in TypeScript, React, Redux, GraphQL, WebGL, or similar frontend technologies.

Conditions

Hybrid role. You are expected to work from a specific location at least 3 times a week, or as often as your manager sets.

Travel is required for less than 25% of the time.

Relocation benefits are available for candidates who qualify under company policy.

The base salary range is $174,900 to $261,300. Actual pay within the range depends on factors relevant to the position.

The compensation information is a good faith estimate based on what a successful applicant might be paid under applicable state laws. It may not represent pay for positions outside New York, Colorado, California, or Washington.

You may be required, where applicable, to complete a role-related assessment and/or a pre-employment screening before starting.

The company does not discriminate unlawfully and works to foster inclusion and belonging.

Benefits

• Incentive pay based on company performance, job level, and individual performance.
• Health and wellbeing programs including medical, dental, and vision coverage, a Health Savings Account, Flexible Spending Accounts, a retirement savings plan, sickness and accident benefits, life insurance, and paid vacation and holidays.