Founding Engineer

About Us

Double Blind Bio automates administrative tasks, allowing clinical staff to focus on what matters most: patient care and advancing science. We’re building a future where clinical trials operate more autonomously by connecting sites, sponsors, and their data using AI. Our mission is to accelerate the delivery of cutting‑edge therapeutics from development to market by building intelligent software that streamlines clinical trial operations.

To date, we’ve raised over $7 million in funding from our co‑lead investors, Signal Fire and Define Ventures, to accelerate this vision. Currently, we’re used by over 200 clinical research sites and have a handful of sponsor customers.

We exist to accelerate scientific development and improve human well‑being by increasing the efficacy of medicines worldwide. Today, bringing a drug to market takes over 10 years and $1‑2 billion, with clinical trials consuming the bulk of the time and resources, not drug discovery.

Imagine if you could cut the time and/or cost of running a clinical trial in half. According to the Jevons paradox, as costs drop, demand for running clinical trials would rise, allowing more candidate therapies and more niche treatments to be tested and to reach the market. Clinical trial operations sit at the center of this and are the problem Double Blind Bio is solving for.

As an AI‑focused Software Engineer at Double Blind Bio, you will help build and operate intelligent systems that transform how clinical trials run: reducing administrative burden and accelerating the development of life‑saving medicines. This is a high‑impact, product‑first role where your work directly contributes to improving patient outcomes and advancing scientific research. You will be a core contributor on a fast‑moving, early‑stage team, owning AI‑powered features end‑to‑end and helping shape both the product and technical foundation.

You are a software generalist who brings genuine depth in AI systems—not just in building them, but in measuring, evaluating, and improving them over time. You have strong instincts around when AI is working, when it isn’t, and how to tell the difference. This role is ideal for engineers who thrive at the intersection of AI, healthcare, and product development, and who are energized by high ownership, rapid iteration, and close collaboration with users.

• Design, build, and deploy agentic AI systems that power web experiences and automate clinical workflows
• Own the full eval lifecycle—define what “good” looks like for AI outputs, build evaluation datasets, author metrics, and run structured evals to measure model and pipeline quality
• Instrument AI systems for observability—trace LLM calls, monitor output quality, detect regressions, and build tooling that gives the team visibility into how AI is behaving in production
• Develop and maintain RAG pipelines, prompt chains, and AI‑driven features with a systematic approach to improvement
• Architect and optimize full‑stack applications, including frontend, backend, infrastructure, and data pipelines
• Own features end‑to‑end—from ideation and design through deployment, monitoring, and iteration
• Collaborate with product, founders, and customers to understand needs and deliver impactful solutions
• Contribute to engineering processes, architecture, and best practices as an early team member
• Participate in customer conversations to better understand workflows and continuously improve the product

• Strong full‑stack engineering experience, with the ability to build and ship production‑ready applications
• Genuine depth in AI/ML systems—you understand how models behave, how to evaluate outputs rigorously, and how to think about failure modes beyond “the model got it wrong”
• Experience designing and running evals—you’ve built evaluation sets, defined metrics, and used data to drive decisions about AI system quality
• Strong observability instincts—you know how to instrument pipelines, trace model calls, and build feedback loops that surface problems before users do
• Experience building with LLMs in production (RAG, agents, prompt engineering, fine‑tuning, or similar)
• Pr…