Staff Software Engineer, AI Data Platform

Shape the Future of AI 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. About Labelbox 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 Labelbox is the RL data factory for advancing frontier agent capabilities. We build the data, evaluations, and infrastructure that frontier labs use to train and judge their agents. We're looking for talented, experienced engineers to join us. The bar is high: engineers who have strong judgment and set technical direction, quickly build prototypes that scale into the reliable systems, and are at the frontier of agent-first engineering practices and innovating to accelerate the speed of the business. What you may work on Eval systems that run millions of agent trajectories to measure model and product quality. Fine-tuning pipelines that turn evaluation signals into measurable agent improvements. Agent-first product surfaces: UX and infrastructure for workflows where the user is a model or an agent operator. The systems behind hundreds of thousands of AI interviews used to source and match freelance workers to projects. Infrastructure that scales to the throughput frontier labs actually need. Integration of the latest models and capabilities into production within days of release. What we're looking for 4+ year track record of shipping systems customers and other engineers rely on You build full stack prototypes fast and they hold up. The v1 you ship becomes the foundation the rest of the team builds on. Strong system and API design judgement Hard architecture and product calls land with you. You make them, defend them under pressure, and update fast when someone else is right. You ship production code with coding agents daily. You know where they break and what it takes to make them reliable to further accelerate the team's velocity. You set direction by being the example. Other engineers reach for your designs and your code as the reference. You move fast in ambiguous, startup-pace environments with influence over authority. You have worked in all parts of the stack Deep proficiency in TypeScript and/or Python. Nice to have Production experience building LLM- or agent-driven products. Designing evaluations for LLMs and agents, or producing high-quality data for ML systems. Background in production distributed systems, ML infrastructure, or data systems at scale. 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 $250,000—$280,000 USD Life at Labelbox Location: Join our dedicated tech hub in San Francisco Work Style: Hybrid model with 3 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.