We’re looking for a Full-StackAI 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 FeaturesDesign, 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. ##
Enable Human-in-the-Loop AI Training
Support RLHF and Preference Data Workflows
Leverage LLMs in the Review Loop
Advance AI Evaluation
Create Intuitive, Reviewer-Focused Interfaces
Architect Scalable Data & Service Layers
Solve Ambiguous, Real-World Problems
Ensure System Reliability
Elevate the Team
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, 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. ## Bonus Points
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. ## 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
Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates**is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location.
Annual base salary range
$130,000—$200,000 USD
Life at Labelbox
Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland
Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility
Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
Growth: Career advancement opportunities directly tied to your impact
Vision: Be part of building the foundation for humanity's most transformative technology
Our Vision
We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs. Your Personal Data Privacy: Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice. Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
