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Tiny Health is advancing lifelong health, from the first 1,000 days to the last, and addressing chronic disease through precision microbiome science. Founded in 2020 and built by microbiome scientists and physicians, our testing platform reveals whether your microbiome is trending toward resilience or imbalances using shotgun metagenomics, proprietary AI, and one of the world's largest longitudinal datasets. Trusted by families and health practitioners alike, our research-backed gut and vaginal tests are redefining the microbiome as a cornerstone of personalized health through every life stage. Learn more at tinyhealth.com and poweredbytiny.com.
We are looking for an AI Engineer to join our Product & Engineering team. This role will focus on building and scaling production-grade AI systems that power Tiny Health’s consumer and clinician-facing products. You’ll work closely with our co-founder, product/design lead, and a small, high-performing engineering team to design and implement AI-driven features, pipelines, and infrastructure that drive meaningful health insights.
We are seeking someone who has hands-on experience deploying AI applications in production, beyond prototypes or personal projects. You should be comfortable owning end-to-end delivery — from model integration to infrastructure — and capable of operating independently in a fast-moving environment.
If you’re at the lead level, you’ll also help shape Tiny Health’s AI roadmap, set technical direction, and mentor engineers as we expand our AI capabilities.
Design, build, and maintain production-grade AI systems and APIs that power our products.
Integrate and fine-tune models (LLMs, embeddings, or other ML architectures) for customer-facing and internal use cases.
Own the full lifecycle of AI features — from research and prototyping to productionization, deployment, and monitoring.
Collaborate closely with engineering, product, and science teams to scope and ship impactful AI-driven features.
Design data pipelines, retrieval systems, and scalable infrastructure for inference and model serving.
Ensure performance, observability, and reliability of AI components in production.
(Depending on experience) Lead AI initiatives, guide architecture and system design decisions, and mentor other engineers.
Proven experience building and deploying AI or ML systems in production at scale (not just prototypes or demos).
Deep understanding of model integration workflows — from inference pipelines to prompt engineering, fine-tuning, or RAG setups.
Strong backend engineering background (Python or Node.js preferred).
Experience designing AI architectures (e.g., hybrid retrieval systems, multi-model orchestration, or embeddings).
Experience with AWS cloud infrastructure and data pipelines.
Solid grasp of API development and system architecture for scalable applications.
Comfortable working independently and driving execution in a startup environment.
Experience integrating LLMs into production products (OpenAI, Anthropic, Vertex AI, etc.).
Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma).
Experience with observability, monitoring, and evaluation for AI systems.
Experience leading projects or mentoring engineers.
Prior experience in healthtech, biotech, or working with sensitive health data.
Remote-First Company: our working hours follow a 2 hour window from 9 am - 6 pm CST
Driven by an ambitious mission: we're reinventing how we should think about wellness from the first 1,000 days to the last
Shape what we build, not just how we build it: we prioritize experimentation with taking ownership, and moving fast
No hidden agendas: we operate with full transparency because when everyone's working from the same honest picture, you spend less time on politics and more time on impact
High standards, human culture: we hold ourselves to a high bar and do it alongside a team who can laugh at themselves and make the hard days genuinely enjoyable
Learn fast, be better (root cause analysis)
What that looks like: When a stakeholder gives constructive criticism to a test result, instead of just apologizing and moving on, you dig into why it happened and update the process so the feedback is not received again.
Be relentlessly resourceful (be scrappy)
What that looks like: When a customer asks a question about our science, you you spend time digging through our docs and filtering through Slack for previous replies before pinging the team. You also know not to burn a whole day stuck, so you ask for help with a clear summary of what you've already tried.
Think like an owner (ownership)
What that looks like: When you notice customer engagement is dropping, you don't shrug it off as another team's problem - you flag it, dig into the numbers, and bring a fix, because you're hungry to see the whole company win, not just your own tasks hit their targets.
Act with honesty and empathy (radical candor)
What that looks like: When a colleague shares an idea that carries real risk, challenge in the moment and do it with kindness - don’t vent about it afterward in a side conversation.
Delight people by anticipating their needs (create raving experiences)
What that looks like: You put yourselves in the shoes of the customer or collaborator before presenting an idea, process, or proposal. Think ahead about what their pain and gain points are, and curate your communication to address their needs.
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