--- title: 'Forward Deployed Research Scientist at Labelbox' canonical: 'https://feeny.ai/job/forward-deployed-research-scientist-labelbox-san-francisco-x2c58bs8fmmm' type: 'job' last_seen: '2026-09-07' --- # Forward Deployed Research Scientist at Labelbox - **Company:** Labelbox - **Location:** San Francisco, CA - **Compensation:** $200k–$300k - **Posted:** 2026-04-13 - **Last confirmed live:** 2026-09-07 - **Apply:** https://job-boards.greenhouse.io/labelbox/jobs/5101375007 ## 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 Growth: Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. - Clear 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. ## Role Overview Alignerr is Labelbox's human data organization — we produce the training data that frontier AI labs use to build their most capable models. Our Forward Deployed Research Team sits at the intersection of research science and client delivery, embedding research capability directly into the engagements that drive our business. This is not a traditional research scientist role. You will not spend months pursuing a single research question. You will work on multiple client engagements simultaneously, operating on timescales of days to weeks. You will sit in scoping meetings with research teams at major AI labs, reason scientifically about data strategy in real time, fine-tune open-weight models to validate our data methodology, and collaborate with our Applied Research team to turn client-grounded findings into published work. The pace is fast, the problems are applied, and the feedback loops are short. We are looking for someone who finds that energizing, not compromising. ## Your Impact - Engage directly with frontier lab research teams. You will be in the room during client scoping meetings — not as support staff, but as a technical peer. You'll engage on methodology, challenge assumptions about data requirements, and shape project specifications based on a scientific understanding of how data composition affects model outcomes. Develop deep scientific understanding of client engagements. For each project, you will build a working model of the client's architecture, training methodology, and target capabilities. You'll use this understanding to reason about why a particular data strategy will or won't work, identify risks early, and iterate with empirical grounding — not intuition. Run ablation studies and fine-tune open-weight models. You will fine-tune models on client data (and proxy data) to empirically measure the impact of our data on model performance. This is how we validate that what we deliver actually improves our customers' models — and how we catch problems before the client does. Consult on workflow and quality systems. You will partner with our Human Data Operations team to review annotation schemas, task designs, and quality rubrics before projects go into execution. Your job is to ensure the spec is technically sound — that the data we produce will actually serve the client's training objectives. Collaborate with Applied Research on publications and benchmarks. Our Applied Research team owns the long-horizon research agenda. Your role is to feed them signal from the field — generalizable findings, reusable methodologies, empirical results — and help drive joint projects to completion. You will contribute to benchmarks, white papers, and conference submissions that establish Labelbox's research credibility. ## What You Bring - Required - MS or PhD in Machine Learning, NLP, Computer Science, or a related quantitative field. - Hands-on experience fine-tuning large language models (open-weight models such as Llama, Mistral, Qwen, or similar). - Strong understanding of LLM training pipelines — pretraining, supervised fine-tuning, RLHF/DPO, and how data quality and composition affect each stage. - Experience designing and executing experiments with rigor — hypothesis formation, controlled comparisons, statistical analysis of results. - Ability to operate at speed. You should be comfortable going from problem definition to experimental results in days, not months. - Strong written and verbal communication. You will present findings to client research teams and contribute to published work. Strongly Preferred - Prior experience at a frontier AI lab, applied ML startup, or in a research role with direct client/stakeholder interaction. - Experience with evaluation and benchmarking of LLMs — designing metrics, building eval harnesses, interpreting results critically. - Familiarity with human data pipelines — annotation workflows, quality assurance methodology, inter-annotator agreement analysis. - Experience with reinforcement learning, reward modeling, or RLHF environments. - Published research (conferences, journals, or technical reports) in ML/NLP or adjacent fields. What Matters More Than Credentials - Applied instinct over academic purity. The measure of success here is client impact and publishable-but-practical results — not methodological novelty for its own sake. If your first instinct when handed a problem is to build a framework, this isn't the role. If your first instinct is to run an experiment and get a result, it is. - Comfort with ambiguity and incomplete information. Client engagements rarely come with clean problem statements. You'll need to extract the real question from a noisy conversation, scope an approach quickly, and iterate. - Cross-functional fluency. You will work daily with field engineers, project managers, operations teams, and an independent Applied Research team. Someone who can only operate within a pure research silo will struggle here. - Intellectual honesty. When an ablation study shows the data isn't working, you need to say so — clearly and constructively — even when it's inconvenient for the deal timeline. ## What You Should Know About This Team - We are small and high-leverage. The FDRT is a team of five today. Every person's work directly influences client outcomes and Labelbox's market position. - We operate at the tempo of client delivery. Two-week sprints. SLAs measured in days. If you want months of uninterrupted focus on a single problem, our Applied Research team is a better fit. - We are at the intersection of several teams. FDRT works with Field Delivery Engineers, Human Data Operations, Applied Research, and client research teams. The role requires navigating those interfaces with credibility and without ego. - We protect time for research. 25–30% of team capacity is allocated to research collaboration with Applied Research. This is not aspirational — it is a structural commitment. You will have the opportunity to publish. 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. Annual base salary range $200,000—$300,000 USD Life at Labelbox - Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making - Growth: Career advancement opportunities directly tied to your impact - Vision: Be part of building the foundation for humanity's most transformative technology Our Vision 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. 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](https://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. ## About Labelbox ## Company Overview - **One-liner**: Labelbox builds the data engine for frontier AI, producing environments, expert signals, and platforms that leading AI labs and enterprises use to train, evaluate, and deploy reinforcement learning (RL) models. - **Entity Type**: Private (Funding stage: $189M total funding to date) - **Headquarters**: San Francisco, California, USA (Mission District) - **Founded**: 2018 - **Founders**: Manu Sharma (CEO) – other co-founders not publicly named in available sources. ## Core Business - **Primary industry**: Artificial Intelligence – data infrastructure for RL and frontier model training. - **Target customers**: B2B – frontier AI labs (partnering with over 90% of leading U.S. AI labs) and enterprise teams building specialist AI agents. - **Mission**: "Turn human knowledge into compounding intelligence, making the learning loop the foundation of an AI team’s competitive advantage." ## Products & Services - **Recursion** – RL platform for enterprise specialist models. Integrates enterprise APIs, SaaS tools, databases into simulation environments; enables scenario generation, RL training, evaluation, and deployment. - **Horizon** – RL environments and evaluations for post-training, covering reasoning, tool use, and computer use across autonomous AI research, agent coding, scientific knowledge work, and cybersecurity. - **Alignerr** – Expert network of 2.6M+ knowledge contributors across 200+ domains, providing human preference signals and reward data for frontier models. - **Terra** – Full-stack data products for robotics foundation models (video, trajectories, multimodal annotations) collected with purpose-built hardware and AI-powered diversity engines. ## Market Standing - **Valuation**: Not publicly disclosed. - **Key Metric**: $189M in total funding to date (as of mid-2026). - **Notable Investors/Partners**: Not explicitly listed in available sources; partners include "over 90% of leading AI labs in the U.S." and Meta (case study on GIM benchmark). - **Growth Signals**: - 2.6M+ Alignerr network across 40+ countries. - Published a benchmark with Meta (GIM-615) for evaluating frontier AI reasoning. - Growing headcount: 13 open roles listed on Greenhouse (Applied Research, Engineering, Alignerr Services, Sales). - Expanding from annotation into full RL data engine (Recursion, Horizon, Terra). ## Competitive Advantages - **Unique data engine for RL**: Combines expert human signals (Alignerr) with simulated environments (Recursion, Horizon) and robotics data (Terra) – a full stack for reinforcement learning that few competitors offer. - **Deep partnerships**: Collaborates with leading U.S. AI labs, providing a strong network effect. - **Proven research output**: Co-developed the GIM benchmark with Meta, demonstrating credibility in frontier evaluation. - **Enterprise focus**: Helps companies turn proprietary workflows into training data for specialist models that outperform general LLMs on narrow tasks at lower cost. ## Strategic Focus - Doubling down on reinforcement learning data infrastructure: building environments, reward signals, and evaluation tools for post-training and agentic AI. - Scaling the Alignerr expert network to cover more domains and languages. - Expanding enterprise adoption of Recursion to convert proprietary workflows into production-grade specialist models. ## Why Work Here - **Culture**: "Operate like an early-stage startup, even at our current stage." Fast-paced, high-intensity, high individual ownership, impact over process. - **Location**: HQ in San Francisco’s Mission District – "heart of the AI boom." - **Remote/Hybrid**: Roles listed as "San Francisco Bay Area" – likely office-first with some flexibility (not explicitly stated as remote). - **Perks**: Unlimited PTO, $150/month WFH stipend, $1k annual education stipend, annual travel stipend, paid family leave, medical/dental/vision, HSA/FSA, 401(k) management, employee-paid life & disability. - **Growth**: "Take on expanded responsibilities quickly; career growth tied to impact." Opportunity to work at the cutting edge of AI development with industry leaders. - **Engineering culture**: Emphasis on technical excellence, rapid execution, and solving complex problems in frontier AI. ## Sources 1. [labelbox.com](https://labelbox.com/) – Company homepage, product descriptions, partnership stats. 2. [labelbox.com/company/about/](https://labelbox.com/company/about/) – About page, mission, funding, team, perks. 3. [labelbox.com/company/careers/](https://labelbox.com/company/careers/) – Careers page, work environment, benefits, values. 4. [job-boards.greenhouse.io/labelbox](http://job-boards.greenhouse.io/labelbox) – Current open roles and locations. ## Other roles at Labelbox - [Accounts Payable, Spend Management Coordinator](https://feeny.ai/job/accounts-payable-spend-management-coordinator-labelbox-india-6ev3gkwk5nsb) — India - [Global Specialized Domain Expert Recruiter (Contractor)](https://feeny.ai/job/global-specialized-domain-expert-recruiter-contractor-labelbox-united-states-cggdd2bjm5x0) — United States - [Staff ML Engineer, Agent Training & Environments](https://feeny.ai/job/staff-ml-engineer-agent-training-environments-labelbox-san-francisco-61fyf5x8bnnw) — San Francisco, CA - [Cyber Security Intern](https://feeny.ai/job/cyber-security-intern-labelbox-san-francisco-81atck2vj8rq) — San Francisco, CA - [Targeted Recruiting & Onboarding Specialist (Contractor)](https://feeny.ai/job/targeted-recruiting-onboarding-specialist-contractor-labelbox-india-p380f0dmy14b) — India - [Staff Software Engineer, AI Data Platform](https://feeny.ai/job/staff-software-engineer-ai-data-platform-labelbox-san-francisco-6tgah5atnmsm) — San Francisco, CA - [Deployment Lead](https://feeny.ai/job/deployment-lead-labelbox-san-francisco-na902x0e4rs1) — San Francisco, CA - [Forward Deployed Engineering Manager](https://feeny.ai/job/forward-deployed-engineering-manager-labelbox-san-francisco-pxxp75hvtt47) — San Francisco, CA - [Forward Deployed Engineer, RL Environments](https://feeny.ai/job/forward-deployed-engineer-rl-environments-labelbox-san-francisco-s4zt23ad43kc) — San Francisco, CA