United States
Mobile Modular Portable Storage
Labelbox
San Francisco, California, United States
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.
We’re hiring a Forward Deployed Engineer to own the design, development, and operationalization of reinforcement learning environments. You’ll build the sandboxed, reproducible execution environments that AI agents interact with during training and evaluation—things like terminal-based task benchmarks, browser and computer-use environments, and tool-augmented agentic workspaces.
This is a hands-on engineering role. You’ll write production-quality infrastructure code, integrate with open-source RL tooling, and work closely with our data operations team to ensure environments are robust, observable, and ready for human annotators and model agents alike. You won’t be doing ML research, but you’ll need to deeply understand how RL training loops consume environments and where the bottlenecks live.
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.
We're the only company offering three integrated solutions for frontier AI development:
Alignerr is Labelbox’s human data organization, purpose-built to generate the high-quality training data that powers the next generation of AI models. We partner directly with leading AI labs to produce reinforcement learning environments, evaluation benchmarks, and expert-annotated datasets that push model capabilities forward. Our team sits at the intersection of software engineering, ML infrastructure, and human-in-the-loop data production.
The ideal candidate is a strong software engineer first, with genuine curiosity and working knowledge of reinforcement learning. You’ve probably built infrastructure or developer tooling at a startup or mid-stage company, and you’ve been pulled toward the ML/AI space—maybe through side projects, open-source contributions, or a prior role adjacent to an ML team. You’re the kind of engineer who reads an RL benchmark paper and immediately thinks about how to make the environment more robust, not how to improve the policy gradient.
You thrive in ambiguity. You can take a loosely defined project requirement—“build an environment that tests an agent’s ability to navigate a file system and execute multi-step bash workflows”—and deliver a working, tested, documented system without needing a detailed spec. You move fast, but you care about reliability because you know environments that break silently poison training data.
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.
$140,000—$200,000 USD
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 docs.labelbox.com/page/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.
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