Full-Stack Engineer, Labelbox

• 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

Desirable

• 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
• 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
• 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