Principal Scientist, Biologics Machine Learning

Insilico Search Partners; Boston (MA); On-site; USD 208,000 - 286,000; Full time

Job summary

Insilico Search Partners seeks a Principal Scientist, Machine Learning – Biomolecules to lead AI/ML efforts at the intersection of protein design and agentic automation. You will set technical direction, work with founders and platform engineers, and turn wins into shared libraries across the portfolio.

You will shape method development for protein modeling, apply generative models and LLM-based automation, and mentor a growing team in a fast-paced biotech environment.

Qualifications

• Master’s or PhD in a relevant field with 5+ years of experience in academic, pharmaceutical, or biotech settings; industry AI/ML experience preferred.
• Strong depth in Python and modern ML frameworks (PyTorch or JAX/TensorFlow).
• Experience leading teams of engineers and scientists in fast-paced environments.
• Track record of leadership on AI/ML model progression, via publications, patents, or internal reports.

Responsibilities

• Lead multiple AI/ML and computational projects across early-stage ventures from scoping through production.
• Own method and pipeline development for protein modeling and design, including generative modeling for sequences/graphs/3D structures.
• Design LLM-based agents and agentic workflows to automate protein design and pipelines.
• Drive benchmarking, scaling, and production-grade implementation with rigorous model development and reporting.
• Mentor scientists and engineers, support recruiting and interviewing.
• Represent the team to portfolio companies and external partners as a subject matter expert.

Skills

Python

ML frameworks

Leadership

Publications/impact reporting

Education

Master’s or PhD in ML, math, statistics, computational sciences

Job description

Principal Scientist, Machine Learning – Biomolecules

Location: Cambridge, MA

Salary Range: $208,000 – $286,000

About the Company

Our client is an AI-first venture builder that creates and scales new companies in human health, sustainability, and beyond, applying recent advances in machine learning and data to accelerate fundamental research.

Lead AI/ML projects at the intersection of protein design and agentic automation across early‑stage ventures. This is a hands‑on scientific leadership role for a self‑directed “serial deep diver”: someone who can build generative and structure‑based methods for designing biomolecules, then spin up LLM‑based agents that automate the scientific workflows around them. You will set technical direction, work directly with founders and platform engineers, and turn one‑off wins into shared libraries adopted across the portfolio.

Key Responsibilities

• Lead several AI/ML and computational projects across early‑stage ventures, from scoping through production, within agreed budgets and timelines.
• Own method and pipeline development for protein modeling and design, including generative modeling (diffusion, flow, VAEs) for sequences, graphs, or 3D structures.
• Design LLM‑based agents and agentic workflows that automate protein design, docking, and mass spec pipelines.
• Drive benchmarking, scaling, and implementation into production‑grade systems, with rigor in model development and reporting.
• Manage and coordinate scientists and engineers, mentor early hires, and support recruiting and interviewing.
• Scout emerging literature, propose new development strategies, and represent the team to portfolio companies and external partners as a recognized subject matter expert.

Basic Qualifications

• Master’s or PhD in a relevant field (machine learning, mathematics, statistics, computational sciences) with 5+ years of scientific, engineering, or computational experience in academic, pharmaceutical, or biotech settings; industry AI/ML experience preferred.
• Depth in Python, modern ML frameworks (PyTorch or JAX/TensorFlow), and deep learning architectures.
• Experience driving results through teams of engineers and scientists in fast‑paced, entrepreneurial environments.
• Track record of leadership on AI/ML model progression, reflected in publications, patents, or high‑impact internal reports.

Preferred Qualifications

• Hands‑on protein modeling and design, and LLM/agentic automation of scientific workflows.
• Breadth across proteomics/mass spec, cheminformatics/docking/ADMET, and biophysics/MD.
• Generative modeling (diffusion/flow/VAEs) for sequences, graphs, or 3D structures; docking rescoring (e.g., gnina, DiffDock) and pose quality metrics.