Founding Engineer - Tech Lead
Confirmed on the employer's own hiring board on Nov 12, 2026. First seen by Alion on May 3, 2026.
About Us
Double Blind Bio automates administrative work so clinical staff can focus on patient care and advancing science. We're building a future where clinical trials run more autonomously by connecting sites, sponsors, and their data with AI, accelerating the path from drug development to market. We've raised over $7 million from co-lead investors SignalFire and Define Ventures. We're used by 400+ clinical research sites and and growing.
The Vision
We exist to accelerate scientific development and improve human well-being by making medicines more effective, worldwide. Bringing a drug to market today takes over 10 years and $1-2 billion, most of it spent on clinical trials, not drug discovery. Now imagine cutting that time and cost in half. Per the Jevons paradox, as costs drop, demand rises: more candidate therapies and niche treatments could reach the market. Clinical trial operations sit at the center of that shift, and that's the problem we're solving.
About the Role
Double Blind Bio is looking for a Staff-level product engineer to serve as Tech Lead. You'll stay hands-on, writing and reviewing code daily and owning AI-powered features end-to-end, while acting as technical anchor for a small, growing team: setting day-to-day direction, unblocking teammates, and giving feedback that helps people grow. This is technical and organizational leadership, not people management.
You bring genuine depth in AI systems: not just building with LLMs, but measuring, evaluating, and improving them in production. You know when AI is working, when it isn't, and how to tell the difference, backed by evals wired into real systems rather than notebook experiments. You're a strong partner to product, GTM, and founders, helping shape solutions and pushing back on scope and sequencing rather than just taking specs. This role suits engineers who thrive at the intersection of AI, healthcare, and product, and who want high ownership, rapid iteration, and close collaboration with users.
Responsibilities
Design, build, and deploy agentic AI systems that power web experiences and automate clinical workflows
Own the full eval lifecycle in production: define what "good" looks like, build evaluation datasets, author metrics, and wire evals into CI, monitoring, or release gating
Instrument AI systems for observability: trace LLM calls, monitor output quality, detect regressions, and build tooling for visibility into production behavior
Architect and optimize full-stack applications: frontend, backend, infrastructure, and data pipelines
Partner closely with product, founders, and customers: shape solutions and push back on scope and sequencing rather than just executing specs
Help establish best-in-class engineering processes, architecture, and best practices
Participate in customer conversations to understand workflows and improve the product
Qualifications
Staff+ level product engineer with deep, current hands-on ability. You still write and review code daily
Has built LLM-powered product features and has production evals experience: designed eval sets, defined metrics, and wired them into CI, monitoring, or release gating, not just offline experiments
Full-stack ownership: can carry a feature end-to-end, not just one layer
Has led a team's day-to-day before, as tech lead, project lead, or de facto anchor, keeping a team on the right work and giving feedback that helped people grow
Strong PM and GTM collaborator: partners on shaping solutions and pushes back on scope and sequencing, not just taking specs
Strong observability instincts: instrument pipelines, trace model calls, and build feedback loops that surface problems before users do
Comfort working across disciplines and learning new domains (healthcare, life sciences, data systems)
Ability to work in a fast-paced, evolving environment with high ownership and autonomy
Strong communication skills and willingness to collaborate across teams and with customers
Preferred
Small startup experience (seed/Series A, small teams, high ambiguity)
Has been the senior person on a junior team and raised its bar
Genuine interest in bio/life sciences
Experience with ML evaluation frameworks or LLM observability tooling (e.g., LangSmith, Braintrust, Weights & Biases, Honeyhive, or similar)
Familiarity with statistical thinking around model evaluation: confidence intervals, human-in-the-loop review, A/B testing
Experience building agentic workflows or multi-step AI pipelines