Forward Deployed Research Scientist

Labelbox; San Francisco (CA); Hybrid; USD 140,000 - 200,000; Full time

Job summary

Labelbox is seeking a highly skilled individual to join the Forward Deployed Research Team in San Francisco. This position focuses on producing quality training data for AI models. The candidate should possess an MS or PhD in a quantitative field, with expertise in fine-tuning large language models and a strong grasp of LLM training pipelines. The role includes direct engagement with research teams at major AI labs, impacting client outcomes directly, and involves growth opportunities tied to performance.

Qualifications

• MS or PhD in a related quantitative field required.
• Experience fine-tuning large language models like Llama or Mistral.
• Expertise in LLM training pipelines for optimal data quality.

Responsibilities

• Engage in client scoping meetings as a technical peer.
• Shape project specifications based on scientific understanding.
• Collaborate with Applied Research to publish findings.

Skills

Hands-on experience fine-tuning large language models

Strong understanding of LLM training pipelines

Strong written and verbal communication

Ability to operate at speed

Education

MS or PhD in Machine Learning, NLP, Computer Science, or related field

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.

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.

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.
• Comfort with ambiguity and incomplete information.
• Cross-functional fluency.
• Intellectual honesty.

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.
• 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. You will have the opportunity to publish.

Salary

Annual base salary range: $140,000—$200,000 USD. 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.

Life at Labelbox

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

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.

Contact Safety Note

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.