Sr. 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.

Why Join Us

High-Impact Environment : We operate like an early-stage startup, focusing on impact over process.

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 Sr.

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 Large Surface: Design, build, and ship workflows spanning frontend UI, APIs, backend services, databases, and production infrastructure across a variety of features. Enable Human-in-the-Loop AI Training: Build systems that allow humans to efficiently create, review, and curate high-quality AI training and evaluation data sets.

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. 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 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 stack.