Forward Deployed Research Scientist

WeHireYou; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Labelbox is seeking a Forward Deployed Research Scientist to join our team. You will work at the intersection of research science and client delivery, embedding research capability directly into client engagements to drive data-centric AI solutions.

You will not pursue a single long-term question; instead you will handle multiple client engagements on timescales of days to weeks, shaping data strategy and validating methodologies in real time.

Qualifications

• MS/PhD in ML/NLP/CS or related field.
• Hands-on fine-tuning of large language models.
• Strong understanding of LLM training pipelines.
• Experience designing rigorous experiments.
• Ability to move from problem to results in days.
• Excellent written and verbal communication.

Responsibilities

• Engage directly with frontier lab research teams and clients.
• Build working models of client architectures and training methodologies.
• Fine-tune open-weight models on client data and proxy data.
• Collaborate with Applied Research on publications and benchmarks.
• Review annotation schemas, task designs, and quality rubrics prior to execution.

Skills

LLM fine-tuning

Experiment design

Data quality understanding

Communication

Rapid problem solving

Education

MS or PhD in ML/NLP/CS

Tools

LLM Training Pipelines

Experimentation Frameworks

Job description

Forward Deployed Research Scientist

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.

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