Founding Engineer

What if you got to build the technical foundation of the company redefining health and longevity?

About Helios

Helios is the human enhancement company. Almost all of medicine today is reactive, which means it waits for disease to appear and then tries to treat whatever shows up. We think the interesting problem is the opposite one: seeing trouble early enough to do something about it. That takes a kind of data the healthcare system has never collected, so we are building the instrument ourselves.

That instrument is an at-home panel measuring over 1,000 biomarkers from a single draw, the highest-resolution at-home diagnostic available anywhere. Each draw becomes part of a longitudinal dataset connecting a member's biomarkers with their wearable data, their labs, their records, and how they actually live. We use that dataset to train the Helios Foundation Model (HFM), our path to medical superintelligence. Today the HFM simulates physiology and predicts disease risk, and soon it will design interventions outright.

The three founders are Harvard Medical School physician-scientists (two of us helped pioneer CRISPR diagnostics). We are well-capitalized and building a consumer company first. Roon (takeroon.com), our cognitive performance pouch, is our first product and is live now. Our seed round is backed by Y Combinator and Formation, joined by angels including the chief innovation officer of a top Harvard teaching hospital, founders behind landmark digital health and biotech exits, and a Y Combinator partner.

The role

You would be one of the first employees, and the only person whose full-time job is engineering. Everything technical runs through you: the models, the infrastructure they live on, the security that wraps them, and the product a member actually touches. In practice you would operate as our technical lead from day one, and the role should grow into formal leadership as the team does.

We can cover the clinical and scientific side ourselves. What we need is the person who turns the science into systems that survive contact with production, ideally someone who has already built consumer health software, whether telehealth, at-home testing, or digital health, and knows firsthand why it is harder than ordinary consumer software.

This is not a management job. There is no team to manage yet. It starts hands-on and stays that way for a long while.

What you'll own

1. The website and member product. The platform for the test: the website where someone learns what Helios is and orders a kit, and the member product on web and mobile where they connect wearables and records and read their results. It carries a member through the whole test: ordering, kit shipping and tracking, sample return, lab results, clinician review, and the release of results. Every step has to be reliable, because a lost kit or a late result is a broken promise to a real person. You build the LLM agents that turn model outputs into language a person can act on, and the evaluation harnesses that keep them honest, because a fabricated number in a health report is the one failure we cannot tolerate.

2. The backend. The APIs, services, and integrations that take a member from checkout through lab-partner ingestion to results delivery, with monitoring and observability deep enough to chase a problem from a model output down to a frontend bug.

3. Data security. We hold sensitive health data belonging to real people, so security is a design constraint from the first commit. Encryption, access control, audit logging, and secrets management run through you, vendors are held to the same standard, and the system operates at HIPAA-grade requirements.

4. Serving models at scale. Research code becomes production inference, and what serves a thousand members today needs to serve a hundred thousand without a rewrite, with latency, reliability, and cost always in view.

5. Data and training infrastructure. Pipelines from lab-partner ingestion through QC and normalization, datasets versioned so a result from six months ago can be reproduced, and training infrastructure that stays maintainable as wearables, records, and new assays come online.

6. Training the models. Models trained on our biomarker panel, validated with care, because when an output feeds someone's health decisions, leakage and miscalibration stop being academic concerns.

Who you are

We are hiring one extraordinary engineer, not filling a role. Show us what you have shipped and owned; that counts for more than where you worked or went to school.

• Track record. You have built systems end to end that real users depended on: a product in production, a model you trained and operated, a system that survived scale
• Consumer health experience. You have built a health product that real patients or members use: telehealth, at-home testing, diagnostics, or digital health. You already know what makes this software hard: lab orders and results moving through partner labs, clinician review before anything reaches a member, consent and identity checks, state-by-state rules, keeping PHI out of logs, and the fact that a wrong number on a results page is a real harm, not a bug report. Strongly preferred, though not required
• A spike. Unusually deep in at least one of ML, backend and infrastructure, security, or product engineering, and hungry to cover the rest
• High learning rate, bias to action, and low ego: model training in the morning, a React bug in the afternoon, an incident runbook at night
• Four or more years building production software, though exceptional work overrides the number
• A degree in computer science or a related field, or equivalent depth you taught yourself
• Real production ML experience, including the unglamorous parts like leakage checks and calibration on high-dimensional data
• Strong Python and backend fundamentals, including API design and cloud infrastructure on AWS or GCP
• Hands-on LLM engineering, from prompt design through agent architecture and evaluation
• Enough TypeScript and React to ship real interfaces without help
• Hands-on data-security experience with sensitive or regulated data. This is a hard requirement
• A workflow built around the current generation of AI tools, used to raise your ceiling rather than lower the bar

Bonus points

• Omics or bioinformatics experience, or comfort with high-dimensional biological data
• Healthcare integrations and compliance: lab-partner and EHR interfaces (HL7, FHIR), wearable APIs, HIPAA programs, and BAAs with every vendor that touches PHI
• MLOps and observability tooling for production ML
• Founding-engineer or early-startup experience, including a company of your own
• Shipped a consumer product people chose to use, not had to use
• Open-source work or public technical writing we can read

Why this role

Ownership. The entire technical foundation of the company would be yours, with the authority to make the calls. You work directly with the three founders, your code reaches users within your first few weeks, and if Helios becomes what we intend, the systems underneath it will be the ones you built.

Details

• Full-time
• Boston preferred. We can help cover relocation, and we are open to remote for the right person
• Competitive salary plus meaningful equity

The interview

You meet each of the three founders. If we both like what we see, we bring you in for a paid one-week work trial building something real with us. If it clicks, we make you an offer.

How to apply

Send whatever you are proudest of building, plus your CV or résumé, to hello@heliosinc.ai. What was it? What made it hard? What did it produce? How did you use AI tools while building it? Full posting: heliosinc.ai/engineer

The bar is high, but do not screen yourself out. If you suspect you might be exceptional, write to us anyway.

Helios is an equal-opportunity employer. We do not discriminate on the basis of any characteristic protected by applicable law, and we welcome applicants from every background.