Collate

AI Engineer

San Francisco · Exec

Sponsorship not specified$150k-$300kDetected 560 days ago

Machine LearningNLPLLMsMedical DevicesResearch

About the role

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.

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.
Create tools and workflows that make AI development more efficient across the team.
Hands-on experience building and deploying ML/AI systems in production.

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.
Ownership: Shape both our product experience and our engineering culture from the start.
Experiment rapidly, while balancing iteration speed with the rigor required for high-stakes applications.

The base salary range for this role is $150,000-$300,000 USD annually, depending on experience and level (Tier 1, San Francisco)

Join a company early enough to have meaningful equity and career-defining impact.
Impact: Build systems and experiences that touch real patients and providers, improving healthcare outcomes.
Learning: Collaborate with a uniquely interdisciplinary team-AI researchers, healthcare leaders, and experienced startup builders.

This listing is sourced directly from Collate's careers page and normalized into a canonical job model.