Member of Technical Staff - Forward Deployed Scientist

About the Role

Our Scientific Solutions team is where our science meets our customers. We're hiring forward deployed scientists with industry experience to partner closely with our enterprise customers, raise the scientific bar of our agents, and make sure outputs hold up under real scientific scrutiny.

The role has two parts

One is constructing the bio environment our agents rely on: building and curating the biomedical skills, tools, databases, and data integrations that power them. The other is making sure the science holds up: designing evaluation pipelines and benchmarks, and doing direct scientific review to measure whether agents meet the standards of working scientists. You'll do both while embedded with customers: running training sessions, scoping and delivering solutions on high-stakes projects, and feeding what you learn back into the product.

We're building a team that spans the drug R&D pipeline, so we're hiring across three areas of depth: discovery, IND-enabling, and clinical/translational. You don't need all three; you need to go deep in at least one and be conversant across the rest.

What you’ll do as a Member of Technical Staff - Forward Deployed Scientist at Phylo:

• 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.

What We're Looking For

• 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).