Foundational Model Research Data Scientist

Sapience AI Corporation•Seattle, WA

5d•$204,000 - $216,000

About The Position

Sapience AI is seeking a Foundational Model Research Data Scientist to join their team. This research role focuses on the models that underpin collective intelligence, specifically studying, adapting, and advancing foundational models for language understanding, knowledge representation, and reasoning. The position involves designing experiments, evaluating models, adapting them to professional communities, and integrating findings into the COGENT architecture and MINERVA platform. The role requires scientific rigor to drive product advances in a rapidly evolving field. The quality of Sapience AI's collective intelligence platform is directly dependent on the effectiveness of these foundational models. This role is crucial for separating genuine progress from noise and translating real advances into dependable platform capabilities.

Requirements

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.

Nice To Haves

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.
Industry research experience in a fast-moving AI setting.

Responsibilities

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.
Improve grounding and reduce confident errors in domain settings.
Balance model capability against cost, latency, and production constraints.
Build rigorous evaluation for accuracy, groundedness, safety, and trust, reflecting real community needs.
Keep the organization honest about model capabilities.
Partner with data engineering on datasets for training and evaluation, handling data thoughtfully regarding quality, bias, and protection of sensitive knowledge.
Build the evidence base that makes model claims defensible.
Feed model advances into the neuro-symbolic COGENT architecture and study neural-symbolic method integration.
Help decide where foundational models belong and where structure should carry the load.
Turn research into platform-reliable behavior.
Track the foundational model field, separating real progress from hype.
Bring in advances that matter and set aside those that do not.
Share knowledge to keep the organization current.
Study and reduce failure modes that erode trust, such as hallucination and bias.
Build toward models whose answers members can trust and trace.
Treat safety and trust as part of the research process.