Jobs

At OpenHuman, we’re building a new way to make decisions, grounded in understanding and predicting human behaviour. Our AI models and simulations capture what drives people’s choices, from beliefs and emotions to reasoning and intuition.

OpenHuman was founded by 2 repeat entrepreneurs and the team is from Meta, LinkedIn, Amazon, MIT & Oxford.

We’re looking for a strong full-stack product engineer who has shipped AI-powered features to real customers.

You will own product areas end to end: understand the customer problem, define the approach, build across the stack, evaluate the results and improve the product after release. You’ll work directly with the founders and customers, with meaningful responsibility for both what we build and how we build it.

This role suits an independent self-starter with strong engineering fundamentals, critical thinking and product judgement. You can turn an ambiguous problem into a practical plan, stay focused on the agreed outcome and seek help early when needed.

You should use AI confidently while bringing your own reasoning to the work. We expect you to understand, validate and take responsibility for what you ship.

What You’ll Own

• End-to-end product development. Take a customer problem from discovery through design, implementation and release. Build the frontend, backend services and data integrations needed to deliver a complete, useful experience.
• AI-native product workflows. Design how models, data, conventional software and human review work together. Make thoughtful decisions about where AI adds value and where a simpler, deterministic approach is more appropriate.
• Data and methodological integrity. Understand the sources, transformations and assumptions behind your features. Make outputs traceable to supporting evidence, distinguish observations from model-generated estimates and communicate limitations clearly.
• Evaluation and production quality. Define what good performance looks like and test against realistic cases. Build maintainable software with appropriate testing, monitoring and failure handling, balancing quality, latency and cost.
• Focused delivery and iteration. Break work into achievable milestones, communicate estimates and uncertainty, and flag blockers early. Agree changes in scope or priorities, ship incrementally and improve the product using customer feedback and observed results.

Skills & Requirements

Technical Skills

• Full-stack development: Strong proficiency in TypeScript and React, including building responsive interfaces, managing application state and integrating backend APIs.
• Backend engineering: Proficiency in Python or Node.js, with experience designing APIs, implementing business logic, integrating external services and handling background jobs.
• Databases and data processing: Strong SQL skills and experience with relational databases such as PostgreSQL. Comfortable designing schemas, transforming data, investigating data-quality issues and tracing outputs back to their source.
• LLM applications and agentic workflows: Hands-on experience shipping LLM-powered product features using prompt design, structured outputs, tool calling and multi-step orchestration. Comfortable managing workflow state, failure recovery and human review, with sound judgement about when an agentic approach adds value.
• AI evaluation and reliability: Experience creating evaluation datasets and tests, investigating unsupported or inconsistent outputs, and implementing validation, retries and fallbacks. Understand the trade-offs between model quality, latency and cost.
• Production engineering: Experience with Git, automated testing, CI/CD, Docker and cloud deployment. Able to debug production issues and implement appropriate logging, monitoring, authentication and access controls.

Experience & Working Style

• Engineering experience: Typically 5+ years of professional software engineering experience, or equivalent evidence of independently building and owning substantial production products.
• Shipped AI-powered products: You have delivered and maintained AI features used by real customers—not only prototypes, hackathon projects or experience using AI coding assistants.
• End-to-end ownership: You can take a loosely defined customer problem, clarify requirements, make technical decisions, build the solution and own its performance after release.
• Critical thinking: You can explain how your implementation works, what data it uses, why you chose the approach and how you validated the results. You question AI-generated suggestions rather than accepting them without scrutiny.
• Independent execution: You create a practical plan, identify missing information and make progress without needing every step assigned. You take the initiative to seek help or feedback when needed.
• Prioritisation and communication: You stay focused on agreed outcomes, provide realistic estimates and communicate uncertainty or blockers early. You discuss material changes in scope or direction before pursuing them.

Nice to Have

• Experience as a founder, founding engineer or early-stage startup engineer.
• Experience building analytics products, data visualisations or recommendation systems.
• Familiarity with experimentation, statistical reasoning and the distinction between correlation and causation.
• Experience with subscription businesses, pricing, conversion or retention.
• Experience with retrieval-augmented generation, embeddings or agent orchestration where these were needed to solve a real product problem.