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.

We're the only company offering three integrated solutions for frontier AI development:

• Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale

• Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models

• 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. You'll 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. 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 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 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.

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.

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

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In this role, you'll go beyond using AI tools and help define how we design, build, and iterate on AI-assisted and agentic CI experiences. You'll set standards for what good looks like across our AI agent portfolio, including how we measure success, how we instrument behavior in production, and how we account for large language model limitations. You'll also help responsibly integrate GitLab's Duo Agent Platform into CI workflows at scale, on a foundation that's fast, reliable, secure, and observable.

We have ambitious goals for Agentic CI in FY27. As a Staff Engineer, you will:

• Partner with Engineering, Product, and UX leadership to pressure-test our priorities: where we can move faster, where we're missing data, and where there's whitespace to innovate. Part of this includes learning and growing with the Engineering team you will collaborate closely with.

• Define what success looks like across our agent portfolio and make sure we're tracking against it , not just shipping, but learning.

• Bring a sharp eye to the competitive landscape, helping us understand what it takes to keep GitLab CI best-in-class in an increasingly agentic world.

Examples of Agentic CI work we have planned for the upcoming year:

• AI Pipeline Builder, the foundational CI agent that auto-creates pipelines for new projects and serves as the launchpad for onboarding new CI users.

• Automate the Fix a Failing Pipeline flow at scale – from dogfooding on internal GitLab projects through to safe, controlled rollout for customers, solving real infrastructure and scalability challenges.

• Build the instrumentation and observability layer that makes agentic CI trustworthy , trigger volume dashboards, retry rates, cost safeguards , so we can measure what's working, catch what isn't, and iterate with confidence.

• Harden the CI pipeline execution infrastructure that these agents depend on: database access patterns, background processing, and job orchestration built to handle the additional load that AI-driven automation introduces at enterprise scale.

You'll shape and scale GitLab CI backend infrastructure to improve performance, reliability, and usability for users running jobs at high volume. You'll design and implement AI-powered features for Agentic CI, including agents, agentic flows, and LLM-backed tooling that integrates with GitLab's Duo Agent Platform. You'll define what success looks like for AI in CI before you build, including baselines, measurable outcomes, and clear signals that help the team learn and iterate. You'll build the instrumentation and observability needed to make AI-assisted CI trustworthy in production, including feature behavior metrics, dashboards, and safeguards. You'll own and drive measurable performance improvements across CI systems (for example, database access patterns, background processing, and job orchestration) by forming hypotheses, running experiments, and validating results with data. You'll write secure, well-tested, maintainable Ruby on Rails code in a large monolith, improving existing features while reducing technical debt and operational risk. You'll lead cross-functional technical work with Product, UX, and Infrastructure, influencing architecture and execution across the Verify stage. You'll share standards, patterns, and learnings with other engineers, raising the bar for responsible AI integration and evidence-driven engineering across CI.

This role requires advanced proficiency with Ruby and Ruby on Rails, with experience building and maintaining reliable backend services in a large codebase. You should have strong PostgreSQL skills, including data modeling, query tuning, and scaling large tables through proactive performance investigation and remediation. You should have hands-on experience building, running, and debugging high-traffic production systems, ideally in CI, workflow orchestration, or adjacent infrastructure-heavy domains. You should have practical experience designing and shipping AI-powered backend features and integrations, including sound judgment about large language model limitations and responsible use in production. You should have a data-driven approach to engineering: defining hypotheses, establishing baseline metrics, instrumenting changes, and measuring outcomes against clear success criteria. You should have familiarity with observability patterns and tools (metrics, logging, tracing) to diagnose issues, improve reliability, and guide iteration. You should have strong backend architecture and delivery practices, including secure design, well-tested code, and strategies for safe rollouts and zero-downtime changes. You should have clear written and verbal communication skills, including writing technical proposals and documentation, and collaborating effectively in a remote, asynchronous, cross-functional environment.

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We take care of all aspects of database planning, implementation, and maintenance. We perform physical database implementation, including index tuning, storage management and partitioning, and scoping and integration of new datacenter hardware. We monitor, tune, and refactor database and server performance. We collaborate to implement database-related features, including data modeling, procedural code design, query tuning, and performance management. We design and implement schemes for data security, system availability, and disaster recovery, including backup, log shipping, mirroring and Always On.

We also recruit database specialists like you. Do you prefer to collaborate with peers to define the work that you pursue? Valve's work environment is unique in that we rely on employees to be self-motivated, accountable, and able to recognize where to focus energy. If you're seeking an opportunity to improve upon processes and systems while continuing to call upon your database administration skills, consider joining Valve.

Database administrators at Valve have significant industry experience related to database design, implementation, and maintenance. Typical skills include:

• Expertise with relational database management systems such as Microsoft SQL Server.
• Database administration experience in large-scale high-availability environments.
• Task automation and configuration management with languages such as PowerShell, Python, etc.
• Design and management of disk storage systems
• Capacity planning and system integration
• Networking systems

What We Offer

• An organization where 100% of time is dedicated as groups see fit
• The opportunity to collaborate with experts across a range of disciplines
• A work environment and flexible schedule in support of families and domestic partnerships
• A culture eager to become stronger through diversity of all forms
• Exceptional health insurance coverage
• Unrivaled employer match for our 401(k) retirement plan
• Generous vacation and family leave
• On-site amenities in support of health and efficiency
• Fertility and adoption assistance
• Reimbursement for child care during interviews

Valve strives to improve the diversity of our teams to better serve our diverse global audience. We welcome and encourage individuals from all backgrounds to apply. Candidates will be considered without regard to race, religion, color, national origin, gender, sexual orientation, age, family status, veteran status or disability status. Valve is committed to creating an inclusive work environment and does not tolerate discrimination or harassment in the workplace.

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