Shape the Future of AI
\nAt 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.
\nWe're the only company offering three integrated solutions for frontier AI development:
\nEnterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
\nFrontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
\nExpert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling
\nWhy Join Us
\nHigh-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.
\nTechnical Excellence: Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.
\nInnovation at Speed: We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.
\nContinuous Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.
\nClear 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.
\nThe role
\nWe’re hiring a Forward Deployed Engineering Manager to lead the design, development, and delivery of reinforcement learning environments for agentic AI systems.
\nYou’ll manage a team responsible for building sandboxed, reproducible environments,terminal-based workflows, browser automation, and computer-use simulations,that power both model training and human-in-the-loop evaluation. This is a hands-on leadership role where you’ll set technical direction, guide execution, and stay close to architecture and critical systems.
\nWhat You’ll Do
\nLead, hire, and develop a high-performing team of Forward Deployed Engineers, setting a high bar for ownership, velocity, and technical quality
\nOwn the RL environment roadmap, aligning team execution with customer needs and evolving model capabilities
\nOversee development of sandboxed environments (terminal, browser, tool-augmented workspaces) that support deterministic execution and multi-step agent interaction
\nEnsure reliability, observability, and data integrity through strong instrumentation (logging, trajectory capture, state snapshotting)
\nDrive infrastructure excellence across containerization, sandboxing, CI/CD, automated testing, and monitoring
\nPartner cross-functionally with data operations, product, and leading AI labs to define task design, evaluation protocols, and environment requirements
\nEnable rapid prototyping and iteration, helping the team move from ambiguous requirements to production-ready systems quickly
\nStay close to the technical details,reviewing architecture, unblocking complex issues, and guiding design decisions
\nWhat We’re Looking For
\n5+ years of software engineering experience (Python)
\n2+ years of experience managing or leading engineers in fast-paced environments
\nStrong experience with containerization and sandboxing (Docker, Firecracker, or similar)
\nSolid understanding of reinforcement learning fundamentals (MDPs, reward design, episode structure, observation/action spaces)
\nBackground in infrastructure, developer tooling, or distributed systems
\nStrong debugging skills and systems thinking across layered, containerized environments
\nAbility to operate in ambiguity and translate loosely defined problems into clear execution plans
\nExcellent communication and stakeholder management skills
\nPreferred
\nExperience building or working with RL environments (Gym, PettingZoo) or agent benchmarks (SWE-bench, WebArena, OSWorld, TerminalBench)
\nFamiliarity with cloud infrastructure (GCP or AWS)
\nPrior experience in AI/ML platforms, data companies, or research environments
\nContributions to open-source projects in RL, agents, or developer tooling
\nWhy This Role Matters
\nRL environment quality is a critical bottleneck in advancing agentic AI. Poorly designed or unreliable environments introduce noise into training loops and directly impact model performance.
\nIn this role, you’ll lead the team building the environments that define how models learn,working across a range of cutting-edge projects with leading AI labs. Alignerr offers the speed and ownership of a startup with the scale and resources of Labelbox, giving you the opportunity to have outsized impact on the future of AI.
\nAbout Alignerr
\nAlignerr is Labelbox’s human data organization, powering next-generation AI through high-quality training data, reinforcement learning environments, and evaluation systems. We partner directly with leading AI labs to build the data and infrastructure that push model capabilities forward.
\nLife at Labelbox
\nLocation: Join our dedicated tech hubs in San Francisco or Wrocław, Poland
\nWork Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility
\nEnvironment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making
\nGrowth: Career advancement opportunities directly tied to your impact
\nVision: Be part of building the foundation for humanity's most transformative technology
\nOur Vision
\nWe 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.
\nLabelbox 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.
\nAny 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.
\nXML job scraping automation by YubHub
","url":"https://yubhub.co/jobs/job_fe04c8cc-782","directApply":true,"hiringOrganization":{"@type":"Organization","name":"Labelbox","sameAs":"https://www.labelbox.com/","logo":"https://logos.yubhub.co/labelbox.com.png"},"x-apply-url":"https://job-boards.greenhouse.io/labelbox/jobs/5101195007","x-work-arrangement":"hybrid","x-experience-level":"senior","x-job-type":"full-time","x-salary-range":"$180,000-$220,000 USD","x-skills-required":["Software engineering experience (Python)","Containerization and sandboxing (Docker, Firecracker, or similar)","Reinforcement learning fundamentals (MDPs, reward design, episode structure, observation/action spaces)","Infrastructure, developer tooling, or distributed systems","Debugging skills and systems thinking"],"x-skills-preferred":["Experience building or working with RL environments (Gym, PettingZoo) or agent benchmarks (SWE-bench, WebArena, OSWorld, TerminalBench)","Familiarity with cloud infrastructure (GCP or AWS)","Prior experience in AI/ML platforms, data companies, or research environments","Contributions to open-source projects in RL, agents, or developer tooling"],"datePosted":"2026-04-18T15:56:05.491Z","jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"San Francisco Bay Area"}},"employmentType":"FULL_TIME","occupationalCategory":"Engineering","industry":"Technology","skills":"Software engineering experience (Python), Containerization and sandboxing (Docker, Firecracker, or similar), Reinforcement learning fundamentals (MDPs, reward design, episode structure, observation/action spaces), Infrastructure, developer tooling, or distributed systems, Debugging skills and systems thinking, Experience building or working with RL environments (Gym, PettingZoo) or agent benchmarks (SWE-bench, WebArena, OSWorld, TerminalBench), Familiarity with cloud infrastructure (GCP or AWS), Prior experience in AI/ML platforms, data companies, or research environments, Contributions to open-source projects in RL, agents, or developer tooling","baseSalary":{"@type":"MonetaryAmount","currency":"USD","value":{"@type":"QuantitativeValue","minValue":180000,"maxValue":220000,"unitText":"YEAR"}}}]}