Forward Deployed Engineer, RL Environments
AI summary of the role
Builds and operates reinforcement learning environments for Labelbox's Alignerr team serving AI labs and enterprises.
What you’ll do
- Design, build, maintain sandboxed RL environments for agentic AI training
- Develop reproducible containerized execution environments for deterministic rollouts
- Build instrumentation and observability layers for training and annotation data
What you’ll bring
- >2 years software engineering with Python and one systems language
- Production or near-production containerization/sandboxing experience
- Familiarity with RL concepts: MDPs, reward shaping, episode structure
Technologies
Python · Go · Rust · C++ · Docker · Podman · Firecracker · Gymnasium · PettingZoo · SWE-bench · WebArena · OSWorld
About Labelbox
Three-pillar platform for frontier AI data: enterprise annotation tools, specialized labeling service (Alignerr), expert marketplace for model training and evaluation.
Series D
Source and classification
Internal deployment & tooling · Evidence for this classification:
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. What You’ll Do Design, build, and maintain sandboxed RL environments for agentic AI training—including terminal emulators, browser automation harnesses,
More from the job description
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 G [... source excerpt omitted ...] uation—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. What You’ll Do Design, build, and maintain sandboxed RL environments for agentic AI training—including terminal emulators, browser autom [... source excerpt omitted ...] and reliability: CI/CD pipelines, automated testing of environment configurations, and monitoring for drift or breakage across versions Rapidly prototype new environment types as client and internal requirements evolve, moving from spec to working system in days, not weeks What We’re Looking For Required 2+ years of professional software engineering experience, with strong fundamentals in Python and at least one systems-level language (Go, Rust, C++) Demonstrated experience with containerization and sandboxing (Docker, Podman, Firecracker, or similar) in production or near-production contexts Familiarity with RL concepts: MDPs, reward shaping, episode structure, observati
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