Overview
About Collate
Full job description
About Collate
Collate is an AI document generation platform for life sciences. We automate paperwork with AI, helping our customers get life-saving innovations to patients years faster. Collate is an end-to-end solution, powering every step of drug, diagnostic, and medical device development—from concept to market.
Our CEO Surbhi Sarna is a former General Partner at Y Combinator. Surbhi founded nVision Medical, which developed a new method to detect ovarian cancer and was acquired by Boston Scientific. Our CTO Nate Smith is a former Visiting Partner at Y Combinator and founder of Lever. Our AI researchers, engineers, and designers have worked at Google, Nvidia, Meta, Netflix, Amazon, AirBnB, Hippocratic AI, and Grail, and 40% of our team are former founders.
We’re an elite team, with $125M in funding from top investors (Redpoint, First Round Capital, CRV, Conviction, Y Combinator) and leaders in healthcare and AI. This is a rare chance to join an early-stage company with world-changing potential, experienced founders, and resources to execute at scale.
About the Role
We’re looking for an AI Engineer to help build and productionize the models and systems that power Collate’s products. You’ll work at the intersection of machine learning, software engineering, and product — turning cutting-edge research into reliable, user-facing capabilities.
This is an opportunity to shape how advanced AI is applied in Life sciences — a space where performance, safety, and trust matter as much as innovation. As one of the first AI-focused engineers at Collate, you’ll define the standards for how we evaluate, and deploy models that directly impact drug and medical device development.
Prompt optimization, red-teaming, and improving robustness of LLMs for life-science NLP applications within Collate’s products. Build pipelines and infrastructure to deploy AI systems reliably, safely, and at scale. Collaborate with product, design, and engineering teams to translate user needs into AI-driven features. Develop evaluation frameworks to ensure models are accurate, fair, and trustworthy in real-world life science settings. Experiment rapidly, while balancing iteration speed with the rigor required for high-stakes applications. Create tools and workflows that make AI development more efficient across the team.
Hands-on experience building and deploying ML/AI systems in production. Strong foundation in deploying search and retrieval systems and NLP applications. Ability to bridge research and engineering — from prototyping models to shipping them in user-facing products. Comfort working in an early-stage startup where ambiguity is high and ownership is expected. Motivation to apply AI to life sciences in a way that prioritizes reliability, safety, and impact.
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