Full-Stack Engineer, AI Data Platform

Shape the Future of AI

At Labelbox were building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018 weve 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

Were the only company offering three integrated solutions for frontier AI development:

1. Enterprise Platform & Tools: Advanced annotation tools workflow automation and quality control systems that enable teams to produce high-quality training data at scale
2. Frontier Data Labeling Service: Specialized data labeling through Alignerr leveraging subject matter experts for next-generation AI models
3. 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. Youll 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. Youll be surrounded by curious minds solving complex problems at the frontier of AI.
• Clear Ownership: Youll know exactly what youre responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.

Role Overview

Were looking for a Full-Stack AI Engineer to join our team where youll build the next generation of tools for developing evaluating and training state-of-the-art AI systems. You will own features end to endfrom user-facing experiences and APIs to backend services data models and infrastructure.

Youll 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 reviewerssuch as automated checks critiques ranking suggestions or quality signalswhile 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

• Bachelors 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 inoptimizing databases for performance (e.g. schema design indexing query tuning) and integrating them with broader data workflows.

Engineering at Labelbox

At Labelbox Engineering were 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. Weve 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: with Redux TypeScript
• Backend: TypeScript Python some Java & Kotlin
• APIs: GraphQL
• Cloud & Infrastructure: Google Cloud Platform (GCP) Kubernetes
• Databases: MySQL Spanner PostgreSQL
• Queueing / Streaming: Kafka PubSub

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 humanitys 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 Labelboxs Job Applicant Privacy notice.

Any emails from Labelbox team members will originate from a @ email address. If you encounter anything that raises suspicions during your interactions we encourage you to exercise caution and suspend or discontinue communications.