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Principal ML Scientist, Multimodal Biological Reasoning
Job in
Cambridge, Middlesex County, Massachusetts, 02140, USA
Listed on 2026-07-21
Listing for:
Towards AI, Inc.
Full Time
position Listed on 2026-07-21
Job specializations:
-
IT/Tech
Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Scientist, Data Engineering
Job Description & How to Apply Below
Published 17 Jul 2026### Share this job Cambridge, MA USA
216K - 297K USD Annual Full Time## Role Highlights### Languages used### Key skills
Data EngineerML Research Machine Learning Data ScienceML Scientist Data Exploration Machine Intelligence Computer Science Integrationsfoundation modelLLMsAISustainability Multimodal Architecture Modellingpost -training
Interpretability
PhDQuantitative
AgricultureGDPR### Tools, Libraries and Frameworks## Description The role involves leading efforts to build polyintelligent AI systems that integrate human scientific expertise with machine intelligence and biological signals. The lead will manage a team of machine learning scientists to innovate on model architectures for biological reasoning engines. Responsibilities include sourcing high-value use cases across the organization and its portfolio companies to translate them into technical requirements.
The position requires guiding the development of multi-modal models to discover new biology and engineer biological solutions. Additionally, the lead will oversee the project roadmap from architecture and data exploration through to agentic integration and benchmarking.##
Required Qualifications and Skills Candidates must possess a PhD, MS, or equivalent experience in fields such as computational biology, machine learning, bioengineering, or computer science. The role requires industry-leading expertise in modern LLMs, multimodal modeling, and ML systems, alongside deep experience in AI-for-biology or drug discovery. Applicants should demonstrate the ability to lead high-caliber technical teams and translate complex scientific needs into technical execution plans.
A working knowledge of various biological data modalities, including genomics and protein structure, is necessary. Experience deploying AI systems into scientific workflows at an enterprise scale is also required.
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