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Lightfield is building a Business World Model—a new category of CRM that learns from emails, meetings, and customer conversations to help teams anticipate outcomes, make better decisions, and take action. The company has grown to 5,000+ users since launch and has raised $70M+ from top-tier investors including Andreessen Horowitz, Coatue, Maverick Ventures, and Greylock. The founding team previously built Tome (25M+ users) and brings experience from Llama, Instagram, Google, and Salesforce.
You will join Lightfield Research as a Staff Applied Researcher, reporting directly to Ves Stoyanov, co-founder and Chief AI Officer (previously Facebook AI, co-author of RoBERTa and XLM-R). You'll lead research projects from conception through measurable product impact and publication, working alongside AI engineers who productionize research and GTM experts who define tasks and quality metrics.
Key responsibilities include:
- Leading research projects in evaluation, benchmarks, deal prediction, simulation, and self-improvement
- Designing empirical evaluations of Lightfield's AI system using LLM-as-judge, rubric-based grading, offline replay, and A/B testing on production and offline data
- Building benchmarks for core GTM tasks (list building, CSV imports, mail intelligence, record updates, outbound) and measuring Lightfield against frontier models and competitors
- Creating simulated companies with synthetic users, email streams, meetings, and knowledge bases where ground truth is known, to evaluate the full system end-to-end
- Developing techniques for continuous self-improvement at world, company, and user levels, including reinforcement learning and fine-tuning
- Modeling deal probability and shipping predictive features to production
- Optimizing cost and quality trade-offs across the AI system
- Publishing research blog posts, open benchmarks, arXiv reports, and papers; presenting at conferences
- Building the research team through recruiting and mentoring
You'll help shape the lab's research agenda, culture, and reputation as it scales.
REQUIREMENTS:
- 7+ years of applied research experience in ML or NLP (industry or academia)
- PhD or MS in Computer Science, Machine Learning, or related field (or equivalent research experience)
- Track record of shipped or published research: papers, open-source models, datasets, or benchmarks
- Deep hands-on experience with LLMs, including evaluation, agent design, and retrieval
- Post-training experience (SFT, RL, preference optimization) is a plus
- Rigorous experimental design; ability to distinguish real improvements from noise
- Strong Python; comfortable in production codebases (TypeScript, Node.js, PostgreSQL) alongside engineers
- Ability to drive ambiguous problems independently and balance ambitious goals with frequent, visible results
- Clear communication to technical and non-technical audiences (writing and presentations)
- Energized by building AI that businesses depend on
NICE TO HAVE:
- Experience building simulations, synthetic data, or RL environments
- Experience with forecasting or predictive modeling on business data
- Experience creating benchmarks adopted by others
