Forward Deployed Research Scientist

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.

We're the only company offering three integrated solutions for frontier AI development:

1. Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale
2. Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models
3. Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling

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.

• 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.

• 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.

• 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.

• 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.

• Location: Join our dedicated tech hubs in San Francisco or Wrocław, Poland
• Work Style: Hybrid model with 2 days per week in office, combining collaboration and flexibility
• 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

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.

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.