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Founding Engineer

Boston · Remote possible

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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, and we decided the only way to get it was to build the instrument ourselves. No one has built this before, and we think it is the largest opportunity in healthcare: the chance to build what medicine should have been all along.

That instrument is an at-home panel measuring over 1,000 biomarkers from a single draw, which makes it the highest-resolution at-home diagnostic available anywhere. The test is where we start, not where we stop. Around it we are building the brand, the platform, and the products that we expect will make Helios a household name in health, the company millions of people trust to live longer and better. 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, which today simulates physiology and predicts disease risk, and which we intend eventually to design interventions outright. The first of those interventions go on sale this year, and every one we sell will send outcome data back into the model.

The three founders are Harvard Medical School physician-scientists (two of us helped pioneer CRISPR diagnostics) with backgrounds running from longevity research and computational biology through clinical practice and drug development. We are well-capitalized, and we are building a consumer company first. The products fund the data, the data compounds into models no competitor can train, and those models become our deepest moat. We are not improving the healthcare that exists. We are reinventing it, building a new kind of platform and the company that millions of people trust to monitor and run their health.

The role

You would be employee number one, 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 the first day, and the role should grow into formal leadership as the team does.

We can cover the clinical and scientific side ourselves, since that is where the three of us have spent our careers. What we need is the person who turns the science into systems that survive contact with production. There is no lane to stay inside here, and whatever the company needs built next is yours to build.

What this role is not: a management job. For now there is no team to manage and no process to run, and the work is building the thing rather than overseeing the people who do. The seat can grow into technical leadership over time, but it starts hands-on and stays that way for a long while.

What you'll own

The work splits into six areas on paper, though in practice it is one loop running from raw lab data all the way to the report a member reads on their phone.

1. Machine learning on molecular data

You build and maintain the models at the center of the product, which are trained on data from our biomarker panel. You also own the pipelines that feed them, from lab-partner ingestion through QC and normalization, with datasets versioned carefully enough that a result from six months ago can still be reproduced. Validation deserves special mention, because when a model's output feeds someone's health decisions, leakage and miscalibration stop being academic concerns. As new data sources come online over the next couple of years (wearables and records first, our own genomic and metabolomic assays later), the new models are yours to train as well.

2. Serving at scale

Research code has to become production inference, and what serves a thousand users today needs to serve a hundred thousand without a rewrite. Latency and reliability will live in your head constantly, and so will cost, since inference bills have a way of growing faster than anyone planned for.

3. A security-first backend

We hold sensitive health data belonging to real people, so security is a design constraint from the first commit rather than a compliance exercise bolted on at the end. Encryption, access control, audit logging, and secrets management all run through you, our vendors get held to the same standard, and the whole system operates at HIPAA-grade requirements. At some point something will go wrong anyway, and when it does you run the response, ideally on an architecture you designed so that no single failure can expose very much.

4. The interpretation layer

Our statistical models produce numbers, and numbers are not what a member needs at seven in the morning when results arrive. You build the LLM agents that translate model outputs into language people can act on, and just as importantly, you build the evaluation harnesses that keep those agents honest, because a fabricated number in a health report is the one failure we cannot tolerate. We supply the clinical judgment, and encoding it into what reports say and where they stop is your job.

5. Product, end to end, on web and mobile

The product is everything a member touches. It is the apps and the website where they order a test, connect their wearables and records, and read what their results mean. You build it end to end, on web and mobile, and you make it something people want to open every week rather than a lab report they dread. The founders are technical and will jump in where it helps, but the member-facing product is yours to own and get right.

6. Uptime

Members make health decisions based on what we show them, so the platform staying up is part of the product. Monitoring and alerting sit with you, along with observability deep enough to chase a problem from a model output all the way down to a frontend bug.

Who you are

We are hiring one extraordinary engineer, not filling a role. The bar is simply the best person we can find. Show us what you have shipped and owned, because that will count for more than where you worked or went to school. Specifically, we are looking for:

  • Systems you built end to end that real users depended on
  • Four or more years building production software, though exceptional work overrides the number
  • A degree in computer science or a closely related technical field, or equivalent depth you taught yourself. Real, demonstrable experience matters more than the credential, but we expect the fundamentals
  • Real production ML experience, meaning you have trained models and operated them yourself, and you know 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, including encryption, access control, secrets management, and secure architecture. You treat real people's health data as if it were your own. This is a hard requirement, not a nice-to-have.
  • A workflow already built around the current generation of AI tools, used to raise your own ceiling rather than lower the bar. The work that leaves your hands should be better for how you used them, not noisier. You own the judgment, the taste, and the final result
  • The self-direction to take a vague problem to a shipped solution, and the judgment to pick the right next thing without being told
  • Energized by ambiguity rather than slowed by it, comfortable defining what good looks like before anyone else has

Nice to have

  • Omics or bioinformatics experience, or comfort with high-dimensional biological data
  • Healthcare data standards or compliance work such as FHIR or HIPAA
  • MLOps and observability tooling for production ML systems
  • Founding-engineer or early-startup experience, including a company of your own
  • Vector databases and retrieval systems
  • Open-source work or public technical writing we can read

Who thrives here

The people who do well in a company this small bias toward action and finish what they start, and they would rather learn from real users than from a planning cycle. They see the whole machine without losing the details, and they bring their own ideas instead of waiting for a spec, then argue for them from evidence, with us most of all. They delegate to AI tools the way a good manager delegates to a team, aggressively but with verification, because that is the only way one engineer covers this much ground. They treat member data the way they would want their own treated, and they would sooner miss a deadline than cut a corner on it. Mostly, they want to build something enormous, and they have the patience to spend years doing it. Above all, they build large, complex, yet maintainable systems in a fraction of the time most strong engineers think possible, and they would rather work next to a few people that good than a hundred who are not. The best of them simply get more done than seems possible, taking a system from idea to production in days, doing a team's worth of work alone, and shipping a quarter's worth of roadmap in a week. They run hot and move fast, and they expect the early years of something this ambitious to ask a great deal of them.

Why this role

The honest case for this role over a bigger title or a bigger paycheck somewhere else comes down to ownership. The entire technical foundation of the company would be yours, with the authority to make the calls and the obligation to live with the results. You would work directly with the three founders, with no layers between you and any decision that matters. Your code would reach users within your first few weeks, and if the company becomes what we intend it to become, the systems underneath it will be the ones you built. Your compensation will include meaningful equity ownership. You would build this as an owner, with a real stake in what it becomes.

Details

Commitment: Full-time

Location: Boston preferred. We would love to have you in person with the founders and can help cover relocation, but we are open to remote for the right person.

Compensation: Meaningful equity ownership. You will own a real piece of what you build

How to apply

Send whatever you are proudest of building to hello@heliosinc.xyz, whether that is a repo or a product running in production, along with a few sentences about what made it hard and how you used AI tools while building it. Send your CV or résumé along with it. The work tells us the most, but we want to see the full picture.

The bar is high and we will hold it, but do not screen yourself out. If you are not sure you meet every line above and you suspect you might be exceptional, write to us anyway and let us be the judge.

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.

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