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Location
San Francisco, CA
Salary
$150,000 - $250,000
Type
Full-Time
About Cortex
Cortex is building a neurosymbolic platform for scientific research, and its edge is context almost no one else has: through university contracts it can access entire labs' unpublished, siloed data — the failed experiments and archival files that never make it into a paper or a queryable database. The company is already piloting with top Stanford labs, including geneticist Michael Snyder's, is in talks with a Nobel laureate's lab at Caltech, and has two commercial customers in materials science and therapeutics. Founded by two Yale alumni — CEO Leïa Ryan and CTO Riya Bhargava, who leads the deep ML work — Cortex raised its pre-seed in April 2026 and is preparing its next raise in August.
The platform turns that siloed lab data into structured, queryable context that scientific reasoning models can actually use. At its center is Guild, the data layer: a knowledge graph and ontology (TypeDB) plus a vector store (Qdrant) that model scientific work, ingestion pipelines that populate it, and reasoning agents that query the graph to accelerate discovery — so PhD researchers spend less of their time on data grunt work and more on the science. The company's motto is "making science happen at the speed of imagination." The team works in person in San Francisco.
About This Role
Cortex is hiring a Forward-Deployed Engineer to embed with its design partners — pharma companies and academic labs — and deploy the platform against their real scientific workflows. It's the classic FDE model: embedded with the customer, owns the deployment, routes requirements back to the core team. The founders describe the ideal split as 60% technical / 40% sales — someone equally comfortable writing production code and sitting across from a PhD researcher.
Every partner is different — CMC regulatory review at one, reproduction of published results at another, proteomics or generative compound design at the next — and the data types vary lab to lab, sometimes within a single lab. You'll elicit and document each workflow, extend the ontology and build reasoning agents for partner-specific use cases, write adapters against scientific software and instrument data formats, and build evaluation harnesses that prove correctness on real tasks. Then you route the recurring needs back to the platform team as product requirements.
What You'll Own
- Embed with design partners; elicit and document their workflows (CMC review, result reproduction, proteomics, structural biology, generative compound design) and translate them into platform configuration
- Extend the ontology and build reasoning agents for partner-specific use cases
- Write adapters integrating the platform with scientific software and instrument data formats
- Build evaluation harnesses with domain-appropriate metrics to demonstrate correctness on real tasks
- Prototype fast, deploy iteratively, and manage each deployment to a defined outcome
- Route recurring partner needs back to the platform team as product requirements
Requirements
- Strong general-purpose Python engineering with a real track record of shipping production software
- Enough scientific literacy to collaborate with PhD-level researchers and get productive in an unfamiliar domain fast
Execution and Ownership
- 60/40 technical/sales blend — a genuine builder who's also a strong listener and conversationalist, able to extract what a researcher needs and adapt the product to it
- Owns outcomes; effective working directly with technical customers
- Adapts across wildly different customers — no two labs are alike, especially in the first six months
Background
- No educational-background requirement; new grads through mid-level all in play
- Independent, end-to-end projects (where it's clearly your own work), original CS research, or open-source work read as strong signals
Location and Visa
- SF-based and in person, with willingness to travel to and embed at partner sites
- Sponsorship open (H-1B/L-1 counsel on call)
Nice-to-Have
- Life sciences background (computational biology, chemistry, structural biology, proteomics)
- LLM agents, retrieval, or evaluation frameworks (DSPy or comparable)
- Prior forward-deployed, solutions, or field-engineering experience
- Familiarity with scientific tooling and data formats
- Philosophy / first-principles thinking; enjoys a philosophical conversation
Job Details
Experience
0-5 years
Salary
$150,000 - $250,000
Equity
Yes
Visa Sponsorship
Yes
Employment Type
Full-Time
Benefits & Perks
Health Insurance
Gym Stipend
Doordash For Late-night Work
Green Flags
Prior forward-deployed, solutions, or field-engineering experience (Palantir FDE especially)
Life sciences background (comp bio, chemistry, structural biology, proteomics)
LLM agents / retrieval / eval frameworks (DSPy or comparable)
Red Flags
Pure IC coder with no interest in or aptitude for customer-facing work
Can't get up to speed in an unfamiliar scientific domain; needs everything spec'd out
Group-work resume with no clearly owned end-to-end project
Required Candidate Q&A
Question 1
This role is SF-based and in person, with travel to embed at partner sites — does that work for you?
Question 2
What's your work authorization status, and how soon could you start?
Candidate scorecard
· 8 criteriaStrong general-purpose Python; clear track record of shipping production software
Owns outcomes; effective directly with technical, PhD-level customers
Enough scientific literacy to get productive fast in an unfamiliar domain