Sr. Full-Stack Engineer, AI Data Platform

Labelbox•San Francisco, CA

1d•$180,000 - $260,000•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, or a related field.
3+ 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).
Working knowledge of cloud infrastructure like GCP (GCS, PubSub) and containerization (Kubernetes).
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

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.
Elevate the Team: Re-imagine engineering practices, development processes, and documentation. Share knowledge through technical writing and design discussions.