Member of Technical Staff

What You'll Work On

• Partner with enterprise customers in biotech and pharma to understand their scientific workflows and bring those needs into the product.
• Construct bio environments for our agents: build and curate biomedical skills, tools, databases, and data integrations that expand what agents can do.
• Evaluate agent performance across biomedical domains through internal benchmarks, structured evals, and direct scientific review.
• Validate the scientific accuracy and rigor of agent outputs and drive improvements back into the product with the AI team.
• Deliver in the field: run training sessions, help customers scope and solve real problems, and own delivery from first hypothesis to production.
• Serve as the scientific voice in customer engagements, deployments, and feedback loops.

Requirements

• PhD training in a relevant field (or MD/PhD or equivalent), with strong command of common biomedical tools, databases, and analytical workflows.
• Industry experience in biotech or pharma (computational biology, bioinformatics, or a closely related function).
• Solid engineering skills: writing code, building pipelines, and working with biological data at scale.
• Comfort working directly with enterprise customers and translating their scientific needs into technical requirements, including running training sessions.
• A strong communicator who can explain complex ideas clearly to both scientists and executives.
• Ability to move quickly in a fast-paced research and product environment.
• Deep expertise in at least one of the following areas (and comfort collaborating across the others):
  • Discovery: identifying and optimizing therapeutic candidates across modalities. On the small-molecule side: screening (biochemical, cell-based, or phenotypic), hit-to-lead, and lead optimization; SAR analysis and medicinal chemistry / cheminformatics; chemical and bioactivity data (e.g. ChEMBL, PubChem). On the biologics side: protein and binder design, antibody discovery, de novo protein design, protein engineering, and affinity maturation.
  • IND-Enabling (preclinical): nonclinical safety and toxicology, DMPK, PK/PD modeling, exposure-response and dose selection; familiarity with the regulatory requirements and study designs that support an IND filing.
  • Clinical & Translational: biomarker strategy, mechanism-of-action confirmation, patient stratification, and translational PK/PD; early clinical development (Phase 1/2, first-in-human); real-world evidence and clinical data (EHR, claims, registries; RWD analysis for effectiveness and safety).

Nice to Have

• AI-native working style; fluent with modern AI coding tools and agent-based workflows.
• Experience with LLMs, agents, or AI/ML systems applied to biomedical problems.
• Familiarity with foundation models: genomic foundation models, protein language models, or vision models for pathology/imaging.
• Depth in more than one of the three areas above, or breadth across multiple biomedical domains (genomics, proteomics, drug discovery, clinical data).
• Area-specific strengths, such as: structure-based and generative design of small molecules or proteins, e.g. docking, RFdiffusion, ProteinMPNN, ESM (Discovery); PBPK or QSP modeling, e.g. Simcyp, GastroPlus, NONMEM (IND-Enabling); epidemiology / biostatistics, CDISC data standards, digital biomarkers (Clinical & Translational).