Foundational Model Research Data Scientist

Sapience AI Corporation•Seattle, WA

10d•$204,000 - $216,000

About The Position

Sapience AI is hiring a research-focused data scientist to study, adapt, and advance foundational models that power how its collective intelligence platform understands language, knowledge, and reasoning. The role connects research to the platform by designing experiments, evaluating models, adapting them for professional communities, and incorporating findings into the COGENT architecture and MINERVA platform. The role owns foundational model experimentation, fine-tuning and adaptation, evaluation, model-related datasets, integration with neuro-symbolic methods, awareness of advances in the field, and responsible AI research. It is expected to turn scientific findings into dependable product capabilities, improve model grounding, reduce confident errors, and ensure that model behavior is evaluated honestly for accuracy, safety, and trust. The position is not focused solely on publications, ML infrastructure, data engineering, or public benchmark scores. The scientist will use AI to accelerate literature review, experiments, and analysis while retaining responsibility for judgment and interpretation. They will work closely with Neuro-Symbolic AI, Applied AI, ML Infrastructure, and Data Engineering, and feed model advances into COGENT and MINERVA. The stated base salary is $204,000–$216,000, plus early-stage equity and health and wellness benefits. Sapience AI describes itself as an equal opportunity employer.

Requirements

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 interpretation of results.
Strong Python skills and experience with modern ML frameworks.
Ability to translate research into advances a product can use.
Care for safety, bias, and trust in model behavior.
Clear written communication of technical findings.
Prior research or applied science work on foundational models, LLMs, or NLP.
Track record of experiments that led to real advances or sound decisions.
Experience bridging research and engineering.

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.
Experience with PyTorch or equivalent deep-learning frameworks, LLM training, fine-tuning and serving tools, experiment tracking and evaluation frameworks, retrieval, embeddings and vector systems, and distributed training and cloud or GPU environments.

Responsibilities

Design and run experiments on foundational models relevant to collective intelligence.
Investigate how models understand language, ground answers, and reason over knowledge; define open questions as testable hypotheses and report results honestly.
Adapt foundational models to the language and needs of professional communities, including fine-tuning and alignment where useful.
Improve model grounding and reduce confident errors in domain settings while balancing capability, cost, latency, and production constraints.
Build rigorous evaluations for accuracy, groundedness, safety, and trust that reflect real community needs rather than only public benchmarks.
Communicate clearly what models can and cannot do based on evaluation evidence.
Partner with data engineering on datasets used for training and evaluation, considering data quality, bias, and protection of sensitive community knowledge.
Feed model advances into the neuro-symbolic COGENT architecture and study how neural and symbolic methods work together.
Help determine where foundational models should be used and where structured methods should carry the load.
Track developments in foundational models, distinguish meaningful progress from hype, and share relevant knowledge across the organization.
Study and reduce trust-eroding failure modes, including hallucination and bias, and support traceable, trustworthy answers.
Translate research findings into dependable product behavior in COGENT and MINERVA.
Use AI to accelerate literature review, coding experiments, and analysis while retaining responsibility for scientific judgment and interpretation.
Work with Neuro-Symbolic AI, Applied AI, ML Infrastructure, and Data Engineering.