Help build nutrition software that works in the real world.
Emeal sits at the intersection of metabolic science, local food systems, and applied AI. We are building tools that need to be medically sensible, operationally practical, and simple enough for real households to trust.
What the work feels like
Small team. Hard constraints. Real product surface.
Build systems that can reason within nutritional, budget, and seasonal boundaries instead of producing generic meal plans.
Work on a product that treats local ingredient availability and price as first-class inputs, not footnotes.
The bar is simple: if users follow the output, it has to be coherent, useful, and safe enough to earn trust.
Health impact, not vanity metrics
You would be building software that changes what people actually cook, buy, and eat every week.
High ownership from day one
The team is small. The scope is not. People here own product decisions, implementation, and the quality bar.
Evidence has to survive contact with reality
Nutrition science, local availability, budget constraints, and AI behavior all meet in the same product surface.
India-first problem set
We are building for one of the most complex food and health ecosystems first, then expanding outward.
Role Areas
Where we expect to keep investing
The exact openings will evolve, but these are the workstreams that matter most to the product and company.
Engineering
Full-Stack Product Engineer
Own end-to-end product flows across onboarding, family planning, meal generation, analysis, and growth surfaces.
Applied AI
AI Systems Engineer
Improve prompt pipelines, evaluation loops, constraint enforcement, and nutritional reasoning quality across plan generation.
Nutrition
Nutrition Research and Data Ops
Turn nutrition evidence into guardrails, ingredient coverage, validation checklists, and data the product can trust.
How We Work
The environment
Emeal is not a place to hide inside a narrow function. The work crosses product, AI behavior, nutrition logic, and implementation detail. People who do well here like ambiguity, but they also know how to reduce it.
Hiring Principles
Understand the user constraint set
We care about candidates who can reason about health conditions, affordability, ingredient availability, and product usability together.
Bias toward shipping
Ideas matter less than the ability to turn a fuzzy constraint into a product decision and then into working code or systems.
Raise the technical bar
We value people who make reasoning explicit, challenge weak assumptions, and leave the system cleaner than they found it.