Applied Computational Nutrition Scientist

TL;DR

Applied Computational Nutrition Scientist (AI Food Tech): Design, build, and iterate nutrition codes within an AI-based platform with an accent on scientific modeling, logic architecture, and library governance. Focus on managing interactive modeling loops, refining assumptions through experimentation, and translating scientific logic into product narratives.

Location: United States, Remote

Company

Well-funded food tech AI startup building the world’s most comprehensive food data platform powered by AI and the best nutrition database.

What you will do

• Design, build, and iterate nutrition codes using the proprietary Code Builder in the full interactive modeling loop: Hypothesis → Build → Evaluate → Refine.
• Transition raw food data into structured intelligence by exploring model structures, identifying edge cases, and refining scoring assumptions.
• Own the integrity of the Code Library, ensuring cross-product consistency as the ground truth for AI systems.
• Test model variations, interpret AI-generated structures, and improve accuracy and real-world relevance.
• Lead collaborative code-building sessions with external partners to create shared modeling frameworks.
• Partner with Product team to document scientific logic for simplified consumer narratives.

Requirements

• BS or MS in Nutrition, Food Science, or related field with deep ingredient and food processing knowledge.
• 2–3 years in a technical, analytical, or data-driven role.
• Strong comfort with math, structured logic, quantitative decision rules, thresholds, and scoring systems.
• Ability to work within proprietary computational system, interpreting outputs and refining behavior.
• Proven project coordination: staying organized, tracking details, delivering on time in teams.
• Strong facilitation and communication skills; collaborative, proactive, reliable.
• Mindset of a natural explorer identifying edge cases and refining assumptions through iteration.

Nice to have

• Experience thriving as a model builder shaping system behavior.
• Ability to translate complex nutritional concepts into functional utility.
• Product-centric mindset linking scoring systems to B2B dashboards or consumer apps.
• Operational excellence maintaining data integrity at high speed.