Forward Deployed Research Scientist Labelbox - San Francisco, CA,

Forward Deployed Research Scientist

Labelbox is a data-centric AI platform trusted by world-class organizations to quickly and efficiently launch their initiatives with LLMS, generative AI, and more.

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:

• 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

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.

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.

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  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…