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.
\nWe're the only company offering three integrated solutions for frontier AI development:
\nGet an inside look and hear directly from our current Forward Deployed Engineers here!
\nAbout the Role
\nThe Deployment Lead owns the successful delivery of complex customer programs from initial scoping through steady-state execution , on time, to the quality bar, and at margin. This is a senior, customer-facing role for operators who can translate a customer's technical and business requirements into clear execution plans, coordinate multiple internal workstreams, own the economics of the deployment, and ensure projects meet their throughput, quality, delivery, and margin commitments.
\nDeployment Leads sit at the intersection of customer relationship management, technical program management, and operational and financial execution. They must understand the underlying technical requirements of each program well enough to pressure-test implementation plans and communicate tradeoffs clearly with the customer , and own the unit economics well enough to protect margin while they do it.
\nThe Deployment Lead is the operations-and-margin leg of the customer team: they own delivery and the deployment's economics, partnering with the General Manager, who owns the broader customer relationship and growth. They are the primary point of accountability for the delivery of a major customer or portfolio of projects, and the customer's point of contact when timelines, requirements, or operational performance are in play. They coordinate Technical Program Managers and Forward Deployed Engineers across the account, ensuring each workstream is staffed from the pools, technically sound, and aligned with the broader customer objective.
\nThis role is equivalent in seniority to a TPM Manager, but with a stronger emphasis on customer ownership, deployment success, and margin. It is a senior destination on the TPM track and a pathway toward a General Manager role. General Managers own the broader account end-to-end, including the overall customer and researcher relationships, proposal development, account strategy, operating cadence, and the portfolio of deployments delivered for that customer.
\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:
\nAbout the Role
\nThe TPM Manager leads and grows the team of Technical Program Managers (TPMs) who run the day-to-day execution of our customer data projects. TPMs own the operational core of each project , setup, bringing data trainers on, throughput and fulfillment tracking, project margin, payment tiers, running projects through their gates, and keeping Airtable current as the system of record. The TPM Manager owns the people who do that work: their craft, their growth, and how they're deployed across the portfolio.
\nThis role sits at the intersection of people leadership, operational rigor, and financial discipline. It's a player-coach role: the TPM Manager must understand project operations well enough to coach on setup and tracking, pressure-test delivery plans, and judge where throughput, margin, or quality is at risk before it becomes a delivery issue.
\nThe TPM Manager reports to the Services lead and partners closely with the FDE Manager, Deployment Leads, and General Managers. Because TPMs aren't bound to a single domain and any project with a significant number of data trainers needs one, a core part of the role is owning the staffing cadence , allocating TPMs across a shifting portfolio and reconciling project demand against capacity so every project is staffed and running well.
\nThis role is equivalent in seniority to a Deployment Lead, with a stronger emphasis on managing the TPM team and the operational engine rather than direct customer ownership. The TPM Manager is also the steward of the TPM career path , TPM to TPM 2 to TPM Manager, with a branch to Deployment Lead and, in time, toward General Manager.
\nIn your first several months, you'll take ownership of the TPM team, get every active project appropriately staffed, and raise the bar on project setup, tracking, and Airtable hygiene. You'll get TPM onboarding running smoothly , with new TPMs reaching a solo-ready standard faster , build strong working relationships with the FDE Manager, Deployment Leads, and GMs, and establish clear visibility into throughput, margin, and delivery risk across the portfolio.
\nOver time, you'll define how the TPM function operates at scale, develop TPMs into Deployment Leads and future leaders, and build the playbooks and operating systems that keep the engine running as the organization grows.
","enriched_at":1781035483499},{"id":"job_2dca9776-35f","title":"Technical Program Manager","source_url":"https://job-boards.greenhouse.io/labelbox/jobs/4785366007","location":"San Francisco, CA","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","description":"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.
\nWe're the only company offering three integrated solutions for frontier AI development:
\nAbout the Role
\nThe Technical Program Manager owns the day-to-day execution of our customer data projects. The TPM runs the project on the ground , keeping throughput, delivery, and quality on track from kickoff through steady-state to final delivery. As a rule, any project with a significant number of data trainers on it needs a TPM.
\nTPMs own the operational core of a project: setting it up, bringing data trainers on, running it through its gates (calibration before scaling, staged ramp, delivery acceptance), tracking fulfillment and project margin, operating payment tiers, and keeping Airtable current as the system of record. By owning the running of the project, the TPM frees the Forward Deployed Engineer (FDE) to focus on higher-level technical and customer-facing work, and protects the Forward Deployed Researcher's (FDR) research time.
\nThe TPM sits at the intersection of operations, finance, and people coordination. They aren't bound to a single domain , they work across whatever projects need them. A key part of the role is keeping the FDE and FDR connected as a project kicks off, and orchestrating the data-trainer workforce through Pod Leads, who are central to the TPM's ability to deliver on complex projects.
\nThe TPM reports to the TPM Manager for craft and career, and on the ground takes delivery direction from the Deployment Lead on accounts that have one (the Deployment Lead owns the customer's delivery end-to-end and coordinates TPMs across its projects). It's a senior operating track: TPMs can grow into TPM Manager (managing other TPMs) or Deployment Lead (the customer-facing path), and from there toward a General Manager role.
\nIn your first several months, you'll take ownership of a set of projects, get fluent in the platform tooling, and keep Airtable accurate and up to date. You'll hit throughput, quality, and delivery targets, run your projects cleanly through calibration and ramp without quality surprises, build strong working relationships with the FDEs, FDRs, Pod Leads, and Deployment Leads you work alongside, and become the reliable owner of project operations , the person who keeps the FDE–FDR partnership connected and the data trainers productive.
\nOver time, you'll take on larger and more complex projects, mentor newer TPMs, and build toward TPM Manager or Deployment Lead , and, in time, a path toward General Manager.
\nShape 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:
\nLabelbox 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.
\nOur engineering team works with a modern tech stack designed for scalability, performance, and developer efficiency:
\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.
\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.
","enriched_at":1781035435636},{"id":"job_2d4b13c2-1b4","title":"Forward Deployed Engineering Manager","source_url":"https://job-boards.greenhouse.io/labelbox/jobs/5158989007","location":"San Francisco Bay Area, CA","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","description":"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.
\nWe're the only company offering three integrated solutions for frontier AI development:
\nThe FDE Manager leads and grows the team of Forward Deployed Engineers who own the high-level technical side of our customer data programs. FDEs scope tasks, design the pipelines and measurement that turn a customer's goal into a training signal, write the instructions that guide Alignerrs, and work out what each customer actually needs from their data. The FDE Manager owns the people who do that work , their craft, their growth, and how they're deployed across domains and customers , and is accountable for the technical quality and consistency of what the team produces.
\nThis role sits at the intersection of people leadership, technical depth, and delivery quality. The FDE Manager must understand the technical substance of our projects well enough to coach on scoping and pipeline design, pressure-test instructions, judge whether a project's data will genuinely move the customer's model, and raise the bar on measuring quality early rather than late. It's a player-coach role: you lead people, but you stay close enough to the work to set and defend the craft bar yourself.
\nThe FDE Manager reports to the Services lead and partners closely with the SPL Manager, Deployment Leads, and General Managers. A core part of the role is keeping FDEs at the right altitude , focused on higher-level technical and customer-facing work , and actively handing the day-to-day running of projects to the SPLs and Pod Leads, so the team's most expensive technical talent is never absorbed into project operations.
\nThe FDE Manager also owns FDE onboarding and is the steward of the FDE career path, which runs from FDE to FDE 2 to FDE Manager, with branches toward the Forward Deployed Researcher (FDR) track and, in time, toward General Manager.
\nIn your first several months, you'll take ownership of the FDE team, raise the bar on scoping, pipeline, and instruction quality, and get onboarding running smoothly , eligible projects defined, the instruction and Loom repository in good shape, and new FDEs reaching a staffable standard faster and more consistently. You'll build strong working relationships with the SPL Manager, Deployment Leads, and GMs, keep FDEs well-deployed and at the right altitude, and become the person the team relies on for craft and career growth.
\nOver time, you'll define how FDEs work at scale: the templates, tooling, and playbooks that let the team produce more without rebuilding from scratch, a measurement-and-quality craft bar that surfaces problems early, and a pipeline of FDEs growing
","enriched_at":1781035420188},{"id":"job_d5f768d1-df6","title":"Full-Stack Engineer, AI Data Platform","source_url":"https://job-boards.greenhouse.io/labelbox/jobs/5019254007","location":"San Francisco Bay Area","job_type":"full-time","experience_level":"mid","work_arrangement":"hybrid","category":"Engineering","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.
\nWe're the only company offering three integrated solutions for frontier AI development:
\nWhy Join Us
\nRole Overview
\nWe’re looking for a Full-Stack AI Engineer to join our team, where you’ll build the next generation of tools for developing, evaluating, and training state-of-the-art AI systems. You will own features end to end,from user-facing experiences and APIs to backend services, data models, and infrastructure.
\nYou’ll be at the heart of our applied AI efforts, with a particular focus on human-in-the-loop systems used to generate high-quality training data for Large Language Models (LLMs) and AI agents. This includes building a platform that enables us and our customers to create and evaluate data, as well as systems that leverage LLMs to assist with reviewing, scoring, and improving human submissions.
\nYour Impact
\nDesign, build, and ship complete workflows spanning frontend UI, APIs, backend services, databases, and production infrastructure.
\nBuild systems that allow humans to efficiently create, review, and curate high-quality training and evaluation data used in AI model development.
\nDesign and implement tooling that supports RLHF-style pipelines, including task generation, human review, scoring, aggregation, and dataset versioning.
\nBuild systems that use LLMs to assist human reviewers,such as automated checks, critiques, ranking suggestions, or quality signals,while maintaining human oversight.
\nDesign and implement evaluation frameworks and interactive tools for LLMs and AI agents across multiple data modalities (text, images, audio, video).
\nBuild thoughtful, efficient user interfaces (e.g., in React) optimized for high-throughput human review, quality control, and operational workflows.
\nDesign APIs, backend services, and data schemas that support large-scale data creation, review, and iteration with strong guarantees around correctness and traceability.
\nTranslate loosely defined operational and research needs into practical, scalable, end-to-end systems.
\nParticipate in on-call rotations to monitor, troubleshoot, and resolve issues across the full stack.
\nImprove engineering practices, development processes, and documentation. Share knowledge through technical writing and design discussions.
\nWhat You Bring
\nBonus Points
\nEngineering at Labelbox
\nAt Labelbox Engineering, we're building a comprehensive platform that powers the future of AI development. Our team combines deep technical expertise with a passion for innovation, working at the intersection of AI infrastructure, data systems, and user experience. We believe in pushing technical boundaries while maintaining high standards of code quality and system reliability. Our engineering culture emphasizes autonomous decision-making, rapid iteration, and collaborative problem-solving. We've cultivated an environment where engineers can take ownership of significant challenges, experiment with cutting-edge technologies, and see their solutions directly impact how leading AI labs and enterprises build the next generation of AI systems.
\nOur Technology Stack
\nOur engineering team works with a modern tech stack designed for scalability, performance, and developer efficiency:
\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 $130,000-$200,000 USD
\nLife at Labelbox
\nShape 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.
\nAs an Applied Research Engineer at Labelbox, you will be at the forefront of developing cutting-edge systems and methods to create, analyze, and leverage high-quality human-in-the-loop data for frontier model developers. Your role will involve designing and implementing advanced systems that align human feedback into AI training processes, such as Reinforcement Learning from Human Feedback (RLHF), Direct Preference Optimization (DPO), etc. You will also work on innovative techniques to measure and improve human data quality, and develop AI-assisted tools to enhance the data labeling process.
\nYour Impact
\nWhat You Bring
\nLabelbox Applied Research
\nAt Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.
\nWe foster an environment of intellectual curiosity, collaboration, and innovation. We encourage our researchers to explore new ideas, engage in open discussions, and contribute to the wider AI community through publications and conference presentations. Our goal is to be at the forefront of human-centric AI development, setting new standards for how AI systems learn from and interact with humans.
","enriched_at":1776527740503},{"id":"job_ed5725bb-311","title":"Applied Research Engineer, Agents","source_url":"https://job-boards.greenhouse.io/labelbox/jobs/4829775007","location":"San Francisco Bay Area","job_type":"full-time","experience_level":"senior","work_arrangement":"hybrid","category":"Engineering","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.
\nAs an Applied Research Engineer at Labelbox, you'll sit at the junction of advanced AI research and real product impact, with a focus on the data that makes modern agents work,browser interactions, SWE/code traces, GUI sessions, and multi-turn workflows. You'll drive the data landscape required to advance capable, adaptable agents and help shape Labelbox's strategy for collecting, synthesizing, and evaluating it.
\nCreate frameworks and tools to construct, train, benchmark and evaluate autonomous agent capabilities.
\nDesign agent-focused data programs using supervised fine-tuning (SFT) and reinforcement learning (RL) methodologies.
\nDevelop data pipelines from diverse sources like code repositories, web browsers, and computer systems.
\nImplement and adapt popular open-source agent libraries and benchmarks with proprietary datasets and models.
\nEngage with research teams in frontier AI labs and the wider AI community to understand evolving agent data needs for frontier models and share best practices.
\nCollaborate closely with frontier AI lab customers to understand requirements and guide model development.
\nPublish research findings in academic journals, conferences, and blog posts.
\nWhat You Bring
\nPh.D. or Master's degree in Computer Science, Machine Learning, AI, or related field.
\nAt least 3 years of experience addressing sophisticated ML problems with successful delivery to customers.
\nExperience building and training autonomous agents,tool use, structured outputs, multi-step planning,across browsers/GUI, codebases, and databases using SFT and RL.
\nConstructed and evaluated agentic benchmarks (e.g. SWE-bench, WebArena, τ-bench, OSWorld) and reliability/efficiency suites (e.g. WABER).
\nAdept at interpreting research literature and quickly turning new ideas into prototypes.
\nDeep understanding of frontier models (autoregressive, diffusion), post-training (SFT, RLVR, RLAIF, RLHF, et al.), and their human data requirements.
\nProficient in Python, data science libraries and deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).
\nStrong analytical and problem-solving abilities in ambiguous situations.
\nExcellent communication skills.
\nTrack record of publications in top-tier AI/ML venues (e.g., ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, etc.).
\nLabelbox Applied Research
\nAt Labelbox Applied Research, we're committed to pushing the boundaries of AI and data-centric machine learning, with a particular focus on advanced human-AI interaction techniques. We believe that high-quality human data and sophisticated human feedback integration methods are key to unlocking the next generation of AI capabilities. Our research team works at the intersection of machine learning, human-computer interaction, and AI ethics to develop innovative solutions that can be practically applied in real-world scenarios.
\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
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