Forward Deployed Scientist

About Phylo

Phylo is an applied research lab building agentic intelligence to accelerate discovery for every biomedical scientist. We believe AI agents will fundamentally transform how biomedical research is done. Our fast-growing team brings together researchers and engineers across AI and biology.

Our Scientific Solutions team is where our science meets our customers. We're hiring computational biologists 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 skills our agents rely on: building and curating the biomedical workflows, 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.

• Partner with enterprise customers in biotech and pharma to understand their scientific workflows and bring those needs into the product.
• Construct bio skills for our agents: build and curate biomedical workflows, 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.

• 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, Pub Chem). 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).

• 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, RF diffusion, ProteinMPNN, ESM (Discovery); PBPK or QSP modeling, e.g. Simcyp, Gastro Plus, NONMEM (IND-Enabling); epidemiology / biostatistics, CDISC data standards, digital biomarkers (Clinical & Translational).

• Type:
  Full-time
• Location:
  
  In-person in South San Francisco (some travel to customer sites)
• Start Date:
  
  ASAP

• Competitive salary and equity share in building the future of biomedical discovery
• Full medical, dental, and vision coverage, including free therapy…