Full-Stack Engineer, AI Data Platform

Labelbox•San Francisco, CA

1d•Hybrid

About The Position

At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. About Labelbox We're the only company offering three integrated solutions for frontier AI development: Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling Why Join Us High-Impact Environment: We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. Technical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence. Innovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution. Continuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. Clear Ownership: You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics. Role Overview We’re looking for a Full-Stack AI Engineer to join our team, where you’ll build the next generation of tools for developing, evaluating, and training state-of-the-art AI systems. You will own features end to end—from user-facing experiences and APIs to backend services, data models, and infrastructure. You’ll be at the heart of our applied AI efforts, with a particular focus on human-in-the-loop systems used to generate high-quality training data for Large Language Models (LLMs) and AI agents. This includes building a platform that enables us and our customers to create and evaluate data, as well as systems that leverage LLMs to assist with reviewing, scoring, and improving human submissions. Engineering at Labelbox At Labelbox Engineering, we're building a comprehensive platform that powers the future of AI development. Our team combines deep technical expertise with a passion for innovation, working at the intersection of AI infrastructure, data systems, and user experience. We believe in pushing technical boundaries while maintaining high standards of code quality and system reliability. Our engineering culture emphasizes autonomous decision-making, rapid iteration, and collaborative problem-solving. We've cultivated an environment where engineers can take ownership of significant challenges, experiment with cutting-edge technologies, and see their solutions directly impact how leading AI labs and enterprises build the next generation of AI systems. Our Technology Stack Our engineering team works with a modern tech stack designed for scalability, performance, and developer efficiency: Frontend: React.js with Redux, TypeScript Backend: Node.js, TypeScript, Python, some Java & Kotlin APIs: GraphQL Cloud & Infrastructure: Google Cloud Platform (GCP), Kubernetes Databases: MySQL, Spanner, PostgreSQL Queueing / Streaming: Kafka, PubSub

Requirements

Bachelor’s degree in Computer Science, Data Engineering, or a related field.
2+ years of experience in a software or machine learning engineering role.
A proactive, product-focused mindset and a high degree of ownership, with a passion for building solutions that empower users.
Experience using frontend frameworks like React/Redux and backend systems and technologies like Python, Java, GraphQL; familiarity with NodeJS and NestJS is a plus.
Knowledge of designing and managing scalable database systems, including relational databases (e.g., PostgreSQL, MySQL), NoSQL stores (e.g., MongoDB, Cassandra), and cloud-native solutions (e.g., Google Spanner, AWS DynamoDB).
Familiarity with cloud infrastructure like GCP (GCS, PubSub) and containerization (Kubernetes) is a plus.
Excellent communication and collaboration skills.
High proficiency in leveraging AI tools for daily development (e.g., Cursor, GitHub Copilot).
Comfort and enthusiasm for working in a fast-paced, agile environment where rapid problem-solving is key.A focus on writing clean, well-tested code and delivering your work on time.

Nice To Haves

Experience building tools for AI/ML applications, particularly for data annotation, monitoring, or agent evaluation.
Familiarity with data infrastructure components such as data pipelines, streaming systems, and storage architectures (e.g., Cloud Buckets, Key-Value Stores).
Previous experience with search engines (e.g., ElasticSearch).
Experience in optimizing databases for performance (e.g., schema design, indexing, query tuning) and integrating them with broader data workflows.

Responsibilities

Design, build, and ship complete workflows spanning frontend UI, APIs, backend services, databases, and production infrastructure.
Build systems that allow humans to efficiently create, review, and curate high-quality training and evaluation data used in AI model development.
Design and implement tooling that supports RLHF-style pipelines, including task generation, human review, scoring, aggregation, and dataset versioning.
Build systems that use LLMs to assist human reviewers—such as automated checks, critiques, ranking suggestions, or quality signals—while maintaining human oversight.
Design and implement evaluation frameworks and interactive tools for LLMs and AI agents across multiple data modalities (text, images, audio, video).
Build thoughtful, efficient user interfaces (e.g., in React) optimized for high-throughput human review, quality control, and operational workflows.
Design APIs, backend services, and data schemas that support large-scale data creation, review, and iteration with strong guarantees around correctness and traceability.
Translate loosely defined operational and research needs into practical, scalable, end-to-end systems.
Participate in on-call rotations to monitor, troubleshoot, and resolve issues across the full stack.
Improve engineering practices, development processes, and documentation. Share knowledge through technical writing and design discussions.