Founding Platform Engineer

Compensation: Competitive salary + meaningful founding equity.

OpenHuman is building a decision platform that helps companies understand how customers, markets, and competitors may respond before a decision goes live. We think there should be a safer environment to test those decisions first. We are not building magic forecasts; we are building auditable AI systems for reasoning about uncertainty before high-stakes decisions are made. Our view is simple: Every answer should be traceable. Every prediction should be testable. Every model should be judged against reality.

The role is a founding engineering position for someone who can build across product, backend, frontend, data workflows, and early infrastructure. You will help build the systems that let us onboard customer data, run AI workflows, store outputs, expose evidence, manage customer workspaces, and turn complex reasoning into clear product experiences. This is not a generic full-stack role. You will be building the product layer that makes OpenHuman repeatable: the user interfaces, APIs, workflow systems, deployment paths, and internal tools that turn powerful AI workflows into software customers can rely on.

What you will do:

• Build the core OpenHuman product across frontend, backend, and data workflows.
• Turn prototype scripts and one-off workflows into reusable product systems.
• Build customer-facing interfaces for querying, exploring, and auditing AI-generated insights.
• Design clear product experiences for complex concepts like evidence, uncertainty, provenance, traces, predictions, and reports.
• Build backend APIs for customer workspaces, data ingestion, workflow execution, auth, and artifact retrieval.
• Design systems for storing source files, runs, outputs, traces, reports, evaluations, and provenance.
• Create internal tools that make customer deployments faster and less manual.
• Own early deployment, observability, reliability, and security foundations.
• Work closely with the Founding AI Engineer to productize AI workflows.
• Work closely with the founders to turn customer pain into product primitives.
• Help define the engineering standards, architecture, and product culture.

What makes this technically hard:

The challenge is not simply building a polished UI on top of an AI system. The challenge is building product infrastructure that makes messy data, uncertain predictions, model traces, source evidence, and customer-facing outputs feel coherent, inspectable, and trustworthy. Customers need to understand what the system said, why it said it, what evidence it used, what assumptions it made, and how the output can be tested against reality. Your job is to make that possible through product, systems, and engineering craft.

What we are looking for:

• 5+ years of software engineering experience.
• Strong backend skills, ideally Python, FastAPI, or a similar stack.
• Strong frontend and product engineering ability, ideally React, Next.js, TypeScript, or equivalent.
• Good SQL and data modelling instincts.
• Experience building data-heavy, workflow-heavy, or B2B SaaS products.
• Comfort deploying and operating production systems.
• Good product judgment: you care about clarity, speed, reliability, and user trust.
• The ability to move fast without creating unnecessary mess.
• Clear communication and a bias toward making complex systems understandable.

Especially relevant experience:

We would be particularly interested if you have worked on any of the following:

• Early-stage B2B SaaS products.
• Data products, analytics tools, workflow tools, internal platforms, or AI products.
• Products with file uploads, artifact storage, versioning, lineage, or audit trails.
• Auth, permissions, rate limits, customer workspaces, and deployment pipelines.
• LLM applications, RAG products, AI copilots, or streaming interfaces.
• Polished interfaces for complex analytical or operational workflows.
• High-reliability products where customer trust depended on transparency and correctness.

Technical areas:

You do not need to know all of these, but the role will touch many of them:

• Python, FastAPI, Pydantic
• React, Next.js, TypeScript
• Postgres and SQL
• Object storage and artifact management
• Background jobs, queues, and workflow orchestration
• Auth, permissions, rate limiting
• Vercel, Railway, Render, Fly, AWS, GCP, or similar
• LLM APIs, streaming responses, embeddings, and vector search

We care more about judgment, product taste, and the ability to build reliable systems than experience with any specific vendor, framework, or cloud provider.

What good looks like:

After your first few months, you might have:

• Built a customer workspace where source data, AI runs, outputs, traces, and reports are versioned together.
• Built a query interface where users can ask questions and inspect cited evidence.
• Replaced a one-off delivery script with a repeatable workflow runner.
• Designed the data model for predictions, source files, model traces, evaluations, and reports.
• Added auth, rate limits, observability, and deployment foundations for early customers.
• Built internal tooling that makes a customer deployment take days instead of weeks.
• Turned a complex AI workflow into a product experience that customers can understand without founder hand-holding.

This is probably not the role for you if:

• You only want to work on frontend or only backend.
• You need mature specs, a large team, and established processes.
• You are uncomfortable with product ambiguity.
• You build beautiful prototypes but do not care about operational reliability.
• You over-engineer infrastructure before the product shape is clear.
• You do not want to understand customer workflows.

OpenHuman is early enough that the first engineers will shape the product, the architecture, and the culture. The opportunity is to build the platform layer for a new kind of AI company: one where predictions are auditable, repeatable, and tested against reality. The AI layer creates the intelligence. The product platform makes it usable, trusted, and scalable. You will have unusual ownership, direct founder access, hard product problems, and the chance to define how customers interact with AI systems built for decisions that matter.

How to apply:

Send us a short note with:

• What you have built that is most relevant.
• A link to your GitHub, LinkedIn, website, or portfolio if useful.
• One example of a product or platform you helped make simpler, more reliable, or more trusted.