{"id":127877,"url":"https://alion.io/job/labelbox-staff-ml-engineer-agent-training-environments","title":"Staff ML Engineer, Agent Training & Environments","company":{"id":48105,"name":"Labelbox","domain":"labelbox.com","url":"https://alion.io/company/labelbox","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":86,"open_postings":7,"ghost_share":0,"stale_share":0.571,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":250000,"max":280000,"currency":"USD","period":"year","gross":null,"usd_annual":280000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GRPO","optional":false},{"name":"LLM","optional":false},{"name":"Post-training","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"SFT","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Databricks","optional":true},{"name":"GCP","optional":true},{"name":"Google Cloud Spanner","optional":true},{"name":"GraphQL","optional":true},{"name":"Java","optional":true},{"name":"JavaScript","optional":true},{"name":"Kotlin","optional":true},{"name":"Kubernetes","optional":true},{"name":"MySQL","optional":true},{"name":"Node JS","optional":true},{"name":"PostgreSQL","optional":true},{"name":"React.js","optional":true},{"name":"Redux","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-07-29T21:20:44Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-10-16T21:38:02Z","board_verified":true,"closed_at":null,"days_open":79,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":78},"description":"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.\nAbout Labelbox\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.\nRole Overview\nLabelbox is the RL data factory for advancing frontier agent capabilities. We build the data, environments, and evaluations that frontier labs use to train and judge their agents.\nThis role sits where training meets infrastructure. You will run the experiments and build the systems that run them: environments agents act in, verifiers that decide whether they succeeded, and the fine-tuning pipelines that turn that signal into a better model. We're looking for someone who does both halves - the engineering throughput of a strong platform engineer, and real depth in post-training agents.\nThe bar is high: engineers with strong judgment who set technical direction, turn prototypes into reliable systems fast, and are at the frontier of agent-first engineering practice.\nWhat you'll work on\nRL environments for agentic tasks: task definitions, tool surfaces, state and reset semantics, reward design - and the harness that runs thousands of them in parallel.\nVerifiers and graders: programmatic checks, LLM judges, rubric pipelines, pass@k scoring. Deciding what \"the agent succeeded\" means, and making that judgment trustworthy at scale.\nFine-tuning pipelines that turn evaluation signals into measurable agent improvements - SFT and RL, from data collection through training to checkpoint evaluation.\nEval systems that run millions of agent trajectories to measure model and product quality.\nTraining and serving infrastructure that scales to the throughput frontier labs need: multi-launcher orchestration, long-running job fault tolerance, cost accounting.\nWhat we're looking for\nAs an engineer\nA 3+ year track record of shipping systems that customers and other engineers still rely on.\nExceptional throughput, without the quality tax. You ship a lot, you review a lot, and the v1 you ship becomes the foundation the rest of the team builds on.\nStrong system and API design judgment. Hard architecture calls land with you: you make them, defend them under pressure, and update fast when someone else is right.\nYou ship production code with coding agents daily. You know where they break and what it takes to make them reliable, and you use that to move the whole team faster.\nYou build the substrate other people's work runs on - tooling, CI, harnesses, libraries - and you treat that as the job, not a distraction from it.\nYou move fast in ambiguous, startup-pace environments, with influence over authority.\nDeep proficiency in Python, and comfort across the rest of the stack.\nAs an RL post-training practitioner\nYou have fine-tuned models for agentic tasks and made them measurably better. SFT plus at least one RL method (GRPO, PPO, DPO, or similar) in production.\nYou have built environments agents operate in, and you know why reward and task design is where most of the difficulty actually lives.\nYou have designed verifiers or graders for open-ended work, and you know how they get gamed.\nYou debug training runs forensically and methodically.\nYou reason about compute-economics. You know what an experiment costs, when a run is not worth finishing, and how to get the same signal for a tenth of the spend.\nYou write up what you learned so it changes what the team does next.\nNice to have\nExperience with agent harnesses and coding agents as subjects of training and evaluation.\nMulti-tenancy and isolation for untrusted agent execution: sandboxing, egress control, credential handling.\nBackground in production distributed systems, ML infrastructure, or data systems at scale.\nExperience working directly with frontier labs or other highly technical customers.\nOur Technology Stack\nOur engineering team works with a modern tech stack designed for scalability, performance, and developer efficiency:\nFrontend: React.js with Redux, TypeScript\nBackend: Node.js, TypeScript, Python, some Java & Kotlin\nAPIs: GraphQL\n Cloud & Infrastructure: Google Cloud Platform (GCP), Kubernetes\nDatabases: MySQL, Spanner, PostgreSQL\nQueueing / Streaming: Kafka, PubSub\nLabelbox 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.\nAnnual base salary range\n$250,000—$280,000 USD\nLife at Labelbox\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.\nYour 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.\nAny emails from Labelbox team members will originate from a @labelbox.com email address. 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