AI Biologics - Genetic MedicinesWe9re seeking an AI Biologist - Genetic Medicines to join our team working at the frontier of what artificial intelligence can achieve in gene therapy, gene editing, and RNA therapeutics development.
About the RoleYou will contribute to our technical approach for evaluating agent performance at reasoning across the genetic medicines pipeline - gene therapy, gene editing, and RNA therapeutics from target validation and drug discovery to clinical development.
You will work as part of a team of software engineers and scientists building datasets and AI agent tasks across the end-to-end genetic medicines pipeline, including:
- Designing realistic program case studies spanning target validation, drug design and screening, chemistry optimization, candidate nomination, and IND-enabling studies across genetic medicine modalities
- Evaluating PK/PD, immunogenicity, and GLP toxicology findings across modalities and development stages
- Reconstructing program go/no-go decisions from multi-source data
- Reviewing and stress-testing agent outputs for scientific soundness, catching reasoning that is textbook-correct but not defensible in a program review
- Calibrating AI agent confidence on preclinical data with incomplete or conflicting signals, rather than forcing a binary call
Requirements- Advanced degree (MS/PhD/PharmD) in molecular biology, genetics, gene therapy, or a related biomedical field, or equivalent industry experience
- 3+ years of hands-on experience in gene therapy, gene editing, or RNA therapeutics (ASO/siRNA/mRNA) discovery or development
- Direct experience with drug design, drug screening, delivery optimization, or IND-enabling study design in at least one genetic medicine modality
- Experience interpreting on-/off-target editing data, biodistribution, immunogenicity, and GLP toxicology to inform dose selection and risk assessment
- Familiarity with IND-enabling regulatory requirements and the nonclinical data package expected by FDA/EMA is a plus
- Experience evaluating potency, specificity, and developability trade-offs across vector, construct, or oligonucleotide candidates
- Strong scientific writing skills, able to produce clear, evidence-supported rationale for a drug development decision
- Proficiency in AI agent use, with the judgment to identify where model reasoning is technically correct but pharmacologically or practically unsound
- Comfort operating in uncertainty, and proactively flagging gaps in data or instructions
- Works with high agency and initiative
Compensation & LogisticsPay: $140K-$180K OTE (on-target earnings), with no ceiling.- Your pay is your OTE times your weekly quota attainment. Hit quota, earn your OTE; double it, earn double. Example: $150K OTE at double quota = $300K.
- Your first 4 weeks (ramp) are paid at full OTE while we train you.
- You9re paid full OTE whenever quota can9t fairly apply: when a new project is starting, the platform goes down, or we pull you onto a special project.
In the US: full-time W-2- Guaranteed base set by your state, paid every paycheck no matter what
- Health, dental, vision: 100% covered for you, 50% for dependents
- 401(k) (no match)
- PTO: 12 days to start, more as you9re promoted
Outside the US: contractor- Paid on performance, with a monthly advance of 60% of OTE that9s never clawed back. US benefits don9t apply.
Growth: two tracks, based on output, not tenure.
- Individual contributor: six levels, so up to five promotions, with OTE rising at each one.
- Management: after your first promotion, you can move into managing a pod if that9s what you want.
Location: fully remote is fine; onsite is preferred, at our Mission Bay waterfront office in SF with catered lunch and dinner and unlimited snacks.
Work authorization: we sponsor H-1B (new and transfers), O-1, and TN, and support STEM OPT.
Interview Process- Apply. If your profile matches, we9ll send you a take-home project.
- Take-home project (HackerRank). We review it as soon as it arrives.
- Technical interview with a Member of Technical Staff (pod lead), scheduled right after your project review.
- Culture interview with our C-suite.
- Offer.
- Background check, once you accept.
