[Hiring] Foundational Model Research Data Scientist @Sapience AI Corporation

Role 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.

Areas of Responsibility

• Foundational model research and experimentation
  • 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.
• Model adaptation and fine-tuning
  • 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.
• Evaluation and measurement
  • 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.
• Data for models
  • 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.
• Integration with COGENT
  • 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.
• Staying at the frontier
  • 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.
• Responsible and trustworthy AI
  • 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.

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.

Preferred Qualifications

• 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 & Competencies

• 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.

Services & Tools Experience

• 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.

Prior Experience & Background

• 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.

Cross-functional Partners

• You work most closely with Neuro-Symbolic AI, Applied AI, ML Infrastructure, and Data Engineering.
• You feed foundational model advances into the COGENT architecture and the MINERVA platform.

Compensation

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