# Member of Technical Staff

**Company:** [Phylo](https://hotfix.jobs/companies/phylo)
**Location:** South San Francisco, CA
**Role:** ML Engineering
**Salary:** $170k – $275k/yr
**Experience:** 5+ years
**Skills:** Phd, Computational Biology, Bioinformatics, Python, Biomedical Databases, LLMs, AI Agents, Protein Design, Pk/Pd Modeling, Cheminformatics, Genomics, Biomarkers
**Posted:** 2026-07-08

> Forward Deployed Scientist partnering with biotech/pharma customers to build biomedical environments and evaluation pipelines for AI agents. Requires PhD, industry computational biology experience, strong engineering skills, and deep expertise in drug discovery, preclinical, or clinical domains.

## Job Description

## 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).

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