Foundational Model Research Data Scientist at Sapience AI in Seattle, WA or US Remote

Job description

• This is a research role focused on the models at the foundation of collective intelligence. You study, adapt, and advance the foundational models that power how Sapience AI understands language, knowledge, and reasoning.
• You work where research meets the platform: designing experiments, evaluating models, adapting them to the demands of professional communities, and feeding what you learn into the COGENT architecture and MINERVA.
• You bring scientific rigor to a fast-moving field, and you turn that rigor into advances the product can actually use.
• The quality of collective intelligence depends on the models beneath it. How well the platform understands a community’s language, grounds its answers, and reasons over knowledge starts with foundational model work done well.
• The field moves quickly, and not every advance is real or ready. Someone has to separate genuine progress from noise and turn the real advances into something the platform can rely on.
• The Foundational Model Research Data Scientist does that. You run the experiments, evaluate honestly, and translate frontier progress into dependable capability for Sapience AI.
• Real advances. Your work improves how the platform understands, grounds, and reasons, in ways members feel.
• Honest evaluation. The organization has a clear, trustworthy picture of what the models can do.
• Better grounding. Confident errors go down, and answers become more traceable.
• Frontier awareness. Sapience AI adopts the advances that matter and skips the ones that do not.
• Research into product. Your findings become dependable behavior in COGENT and MINERVA.
• Trust by design. Safety and trust improve as a result of your research, not despite it.

Responsibilities

• You hold seven areas of responsibility across foundational model research. Each one is yours to set direction on, build, and measure.
• Design and run experiments on foundational models relevant to collective intelligence.
• Investigate how models understand language, ground answers, and reason over knowledge.
• Turn open questions into experiments with clear hypotheses and honest results.
• Adapt foundational models to the language and needs of professional communities, including fine-tuning and alignment where it helps.
• Improve grounding and reduce confident errors in domain settings.
• Balance capability against cost, latency, and the constraints of production.
• Build rigorous evaluation for what matters here: accuracy, groundedness, safety, and trust.
• Design evaluations that reflect real community needs, not just public benchmarks.
• Keep the organization honest about what a model can and cannot do.
• Partner with data engineering on the datasets that training and evaluation depend on.
• Handle data thoughtfully, including quality, bias, and protection of sensitive community knowledge.
• Build the evidence base that makes model claims defensible.
• Feed model advances into the neuro-symbolic COGENT architecture, and study how neural and symbolic methods work together.
• Help decide where a foundational model belongs and where structure should carry the load.
• Turn research into behavior the platform can rely on.
• Track the fast-moving foundational model field and separate real progress from hype.
• Bring in advances that matter and set aside those that do not.
• Share knowledge so the whole organization stays current.
• Study and reduce the failure modes that erode trust, including hallucination and bias.
• Build toward models whose answers members can trust and trace.
• Treat safety and trust as part of the research, not a later concern.

Requirements

• Required qualifications
• A strong research background in machine learning, NLP, or a related field, with a graduate degree or equivalent experience.
• Hands-on experience with foundational models and modern LLMs, including training, fine-tuning, or evaluation.
• Rigor in experiment design, evaluation, and honest interpretation of results.
• Strong Python and modern ML frameworks.
• The ability to turn research into advances a product can use.
• Care for safety, bias, and trust in model behavior.
• Clear written communication of technical findings.
• Foundational model research, fine-tuning, and alignment.
• Evaluation design for accuracy, groundedness, and safety.
• Experiment design and rigorous analysis.
• Grounding and retrieval-augmented methods.
• Working with sensitive data responsibly.
• Translating research into product-ready advances.
• Clear technical writing and communication.
• PyTorch or equivalent deep-learning frameworks.
• LLM training, fine-tuning, and serving tooling.
• Experiment tracking and evaluation frameworks.
• Retrieval, embeddings, and vector systems.
• Distributed training and cloud or GPU environments.
• Python as the primary language, plus data and analysis tooling.
• Integration with the COGENT architecture and the MINERVA platform (trained on the job).
• Prior research or applied science work on foundational models, LLMs, or NLP.
• A track record of experiments that led to real advances or sound decisions.
• Experience bridging research and engineering.
• Industry research experience in a fast-moving AI setting is a plus.
• Publications, patents, or shipped systems in foundational models or applied NLP.
• Experience with retrieval-augmented generation and grounding.
• Familiarity with neuro-symbolic methods and knowledge graphs.
• Experience adapting models to specialized domains.
• Experience handling sensitive or regulated data responsibly.

Skills

Required

• Experiment design and rigorous analysis.
• Working with sensitive data responsibly.
• Strong Python and modern ML frameworks.

Benefits

• Base Salary: $204,000 - $216,000 + early stage equity
• Generous health and wellness benefits