Staff Applied Researcher

Lightfield•San Francisco, CA

3d•$220,000 - $300,000

About The Position

Lightfield is starting Lightfield Research to build a Business World Model through frontier research in simulation, world modeling, and self-improvement. The Staff Applied Researcher will lead the lab’s first projects and help shape its research agenda, culture, and reputation as the team grows. Researchers own projects from the initial question through measurable product impact and publication, working with AI engineers who bring research into production and go-to-market experts who help define tasks and quality. The role focuses on applied research across evaluation, benchmarks, deal prediction, simulation, and self-improvement. The researcher will evaluate and improve Lightfield’s AI system, develop benchmarks and simulated business environments, build predictive product features, and publish research. The position reports directly to Ves Stoyanov, co-founder and Chief AI Officer.

Requirements

At least 7 years of applied research experience in machine learning or natural language processing, in industry or academia.
A PhD or MS in Computer Science, Machine Learning, or a related field, or equivalent research experience.
A track record of research that shipped or was published, such as papers, open-source models, datasets, or benchmarks.
Deep hands-on experience with LLMs, including evaluation, agent design, and retrieval.
Strong experimental design skills and the ability to distinguish genuine improvements from noise.
Strong Python skills and comfort working in a production codebase alongside engineers using TypeScript, Node.js, and PostgreSQL.
Ability to independently drive ambiguous problems and balance ambitious goals with frequent, visible results.
Clear communication skills for technical and non-technical audiences, in writing and presentations.
Interest in building AI that businesses depend on to do their jobs.

Nice To Haves

Post-training experience, including supervised fine-tuning, reinforcement learning, or preference optimization.
Experience building simulations, synthetic data, or reinforcement learning environments.
Experience with forecasting or predictive modeling on business data.
Experience creating benchmarks adopted by others.

Responsibilities

Lead research projects in evaluation, benchmarks, deal prediction, simulation, and self-improvement.
Design empirical evaluations of Lightfield’s AI system using LLM-as-judge, rubric-based grading, offline replay, and A/B tests on production and offline data.
Build benchmarks for go-to-market tasks such as list building, CSV imports, mail intelligence, record updates, and outbound; measure Lightfield against frontier models and competitors.
Build simulated companies with users and streams of email, meetings, and knowledge, with known correct answers, to evaluate the full system end to end.
Develop techniques that help Lightfield improve at the world, company, and user levels, including reinforcement learning and fine-tuning where beneficial.
Model deal probability and its contributing factors, and ship them as product features.
Improve cost and quality trade-offs across the AI system, including inference cost.
Publish research blog posts, open benchmarks, arXiv reports, and papers, and present at conferences and meetups.
Recruit and mentor teammates to help build a world-class research team.