Full-Stack Engineer, AI Data Platform

Shape the Future of AI 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. Your Impact Own End-to-End Product Features Design, build, and ship complete workflows spanning frontend UI, APIs, backend services, databases, and production infrastructure. Enable Human-in-the-Loop AI Training Build systems that allow humans to efficiently create, review, and curate high-quality training and evaluation data used in AI model development. Support RLHF and Preference Data Workflows Design and implement tooling that supports RLHF-style pipelines, including task generation, human review, scoring, aggregation, and dataset versioning. Leverage LLMs in the Review Loop Build systems that use LLMs to assist human reviewers—such as automated checks, critiques, ranking suggestions, or quality signals—while maintaining human oversight. Advance AI Evaluation Design and implement evaluation frameworks and interactive tools for LLMs and AI agents across multiple data modalities (text, images, audio, video). Create Intuitive, Reviewer-Focused Interfaces Build thoughtful, efficient user interfaces (e.g., in React) optimized for high-throughput human review, quality control, and operational workflows. Architect Scalable Data & Service Layers Design APIs, backend services, and data schemas that support large-scale data creation, review, and iteration with strong guarantees around correctness and traceability. Solve Ambiguous, Real-World Problems Translate loosely defined operational and research needs into practical, scalable, end-to-end systems. Ensure System Reliability Participate in on-call rotations to monitor, troubleshoot, and resolve issues across the full stack. Elevate the Team Improve engineering practices, development processes, and documentation. Share knowledge through technical writing and design discussions. What You Bring 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, PubS