Job description

ABOUT TINY HEALTH 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 tinyhealth.com and poweredbytiny.com poweredbytiny.com. THE OPPORTUNITY We are looking for an AI Engineer to join our Product & Engineering team. You'll work closely with our founders, product and design teams, and a tight-knit engineering team to build the AI systems behind our consumer and clinician-facing products. This is a hands-on role focused on taking AI features from idea to production and keeping them dependable once they are live. Because our products sit close to people's health decisions, the work is less about making a model sound convincing and more about building systems whose answers are grounded in a customer's own data and in science our team has reviewed. You'll partner with engineers, product, and our science and clinical teams to turn complex microbiome science into answers people can act on. We're looking for someone who is energized by ambiguity, has shipped AI features that real users depended on, and cares about why a system got something wrong rather than just making the symptom go away. You'll have real ownership over what you build, and room to grow your scope as our AI capabilities expand. WHAT YOU’LL DO - Design, build, and maintain production AI systems and APIs that power our products. - Own AI features end to end, from prototyping and evaluation through deployment, monitoring, and iteration. - Build and improve retrieval and grounding systems so our answers stay accurate and traceable to their sources. - Develop the evaluation and quality tooling that tells us whether a change made an AI system better or worse. - Ensure the performance, reliability, and observability of AI components in production, including triaging and resolving quality issues as they surface. - Collaborate with our science and clinical teams to translate reviewed microbiome science into product experiences people can trust. - Partner with product, design, and engineering to scope and ship high-impact AI-driven features. - Design data pipelines and inference infrastructure that scale as our usage grows. QUALIFICATIONS REQUIRED - 4+ years building and shipping software, including hands-on experience taking ML or LLM-backed features to production. You can walk us through what broke once real users depended on it, and what you did about it. - Solid machine learning fundamentals. You understand how the models you work with actually behave and can reason about their limits, rather than treating them as black boxes. - Working knowledge of retrieval-augmented generation: embeddings, chunking, retrieval quality, and the reasons a retrieval system returns the wrong thing. - Strong backend engineering fundamentals, with experience in TypeScript or Python. - Ability to debug non-deterministic systems, isolating whether a bad output came from retrieval, prompting, tool use, or post-processing. - Treats evaluation as part of building, with experience measuring AI quality rather than relying on spot checks. - Experience with AWS and serverless architectures. - Strong understanding of API development and system design. - Comfortable driving work forward in a fast-paced, remote startup environment. PREFERRED - Familiarity with vector databases (Pinecone, Weaviate, FAISS, Chroma, or similar). - Experience with LLM-as-judge evaluation or human-in-the-loop feedback loops. - Experience with agent tooling, function calling, or MCP. - Experience with observability, monitoring, and alerting systems. - Previous experience in healthtech, biotech, or handling sensitive health data. - Experience fine-tuning and self-hosting models for narrow, latency-sensitive tasks. - Startup or small-team experience where you've worn multiple hats. HOW WE WORK - A mission that actually pulls people in We make the microbiome measurable and actionable at every life stage, and real people write in to say it's life-changing. - Remote-first, real overlap Our FTEs are based in North America and work core hours from 9am–6pm CST, shiftable by up to two hours either way — start at 7am or 11am, wrap at 4pm or 8pm. - Written first, huddle second, meet last Default to a clear Slack post, huddle when a thread stalls, and meet only to make a real decision. - Fast, but never sloppy Do the two-month thing in two weeks, then check the work—shipping errors is rework, not speed. - Shape what we build, not just how we build it There's no playbook waiting for you, so write it—and bring the alternative, not just the objection. - Day One mentality Fewer titles and layers; process is a guardrail where it counts, never the reason something didn't ship. - No hidden agendas Numbers, misses, and feedback are shared openly and in real time, so you always know where you stand. - High standards, genuinely fun people Take the work seriously and ourselves much less—high-performing and fun to be around is the hiring bar. - Is this pace for you? Venture-scaled and on a steep trajectory, with intensity in spikes, fiercely protected flexibility, and faster growth than anywhere else. OUR VALUES 1. Learn Fast, Get Better - Find the root cause, not just the fix, and get better every time 2. Be Relentlessly Resourceful - Dig for the answer, move fast, and know when to ask for help 3. Act Like an Owner, Be Hungry to Win - Act like it’s your company, and do whatever it takes to win 4. Act with Honesty and Empathy - Say what’s true, and say it with care 5. Delight People by Anticipating Their Needs - Solve the problem, then get two steps ahead of it

Requirements

Must-haves

Design, build, and maintain production AI systems and APIs that power our products.
Own AI features end to end, from prototyping and evaluation through deployment, monitoring, and iteration.
Build and improve retrieval and grounding systems so our answers stay accurate and traceable to their sources.
Develop the evaluation and quality tooling that tells us whether a change made an AI system better or worse.
Ensure the performance, reliability, and observability of AI components in production, including triaging and resolving quality issues as they surface.
Collaborate with our science and clinical teams to translate reviewed microbiome science into product experiences people can trust.
Partner with product, design, and engineering to scope and ship high-impact AI-driven features.
Design data pipelines and inference infrastructure that scale as our usage grows.
4+ years building and shipping software, including hands-on experience taking ML or LLM-backed features to production. You can walk us through what broke once real users depended on it, and what you did about it.
Solid machine learning fundamentals. You understand how the models you work with actually behave and can reason about their limits, rather than treating them as black boxes.
Working knowledge of retrieval-augmented generation: embeddings, chunking, retrieval quality, and the reasons a retrieval system returns the wrong thing.
Strong backend engineering fundamentals, with experience in TypeScript or Python.
Ability to debug non-deterministic systems, isolating whether a bad output came from retrieval, prompting, tool use, or post-processing.
Treats evaluation as part of building, with experience measuring AI quality rather than relying on spot checks.
Experience with AWS and serverless architectures.
Strong understanding of API development and system design.
Comfortable driving work forward in a fast-paced, remote startup environment.

Nice-to-haves

Familiarity with vector databases (Pinecone, Weaviate, FAISS, Chroma, or similar).
Experience with LLM-as-judge evaluation or human-in-the-loop feedback loops.
Experience with agent tooling, function calling, or MCP.
Experience with observability, monitoring, and alerting systems.
Previous experience in healthtech, biotech, or handling sensitive health data.
Experience fine-tuning and self-hosting models for narrow, latency-sensitive tasks.
Startup or small-team experience where you've worn multiple hats.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomlllmmachine learningtypescriptpythonawsapi

Platform: ashby
Effort: quick

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