Principal Scientist, Machine Learning
$208,000–$286,000 year
On-siteCambridge, Massachusetts, United States
Job Summary
Lead development and reporting of AI/ML and computational projects across preclinical, translational, and clinical R&D pipelines. Own the build, scaling, and maintenance of agentic systems integrating ML tools for genomics, biomolecule design, and systems biology to accelerate discovery. Manage cross-functional teams of scientists and engineers, mentor early hires, and coordinate project planning including budgets and risk mitigation. Independently scout emerging literature and agentic-AI landscapes to synthesize new development strategies and propose opportunities for venture portfolios. Educate collaborators on operational best practices and present complex findings to diverse audiences to influence technical approaches and project direction.
Required Qualifications
- Master's, or PhD in a relevant field (e.g., machine learning, mathematics, statistics, computational sciences)
- 5+ years' experience scientific/engineering/computational in academic, pharmaceutical, or biotechnology settings
- industry AI/ML experience
- Experience driving results directly or indirectly through teams of engineers/scientists in dynamic, fastpaced, entrepreneurial, and technical environments
- Clear evidence of sustained independent thought and creativity driving high impact, cross disciplinary AI/ML projects
- Successful track record of leadership and contribution to decision making on progression of AI/ML models within projects or programs
- Depth across multiple core tools and concepts, including Python
- Depth across multiple core tools and concepts, including modern ML frameworks (PyTorch or JAX/TensorFlow)
- Depth across multiple core tools and concepts, including version control
- Depth across multiple core tools and concepts, including databases
- Depth across multiple core tools and concepts, including deep learning architectures
- Depth across multiple core tools and concepts, including relevant informatics software
- Consistent record of outstanding performance reflected in publications, patents, or high impact internal reports where applicable
Desired Qualifications
- Breadth across domains such as genomics, protein modeling/design, proteomics/mass spec, multi-omics, cheminformatics/docking/ADMET, biophysics/MD, and scientific literature mining, with the ability to connect them using LLM-based agents and agentic workflows
- Hands-on experience with agentic AI and orchestration frameworks (e.g., Pydantic AI, LangGraph, LangChain, CrewAI, AutoGen)
- Experience integrating LLMs and model platforms, including Anthropic, OpenAI, Vertex AI, and Amazon Bedrock, and building evaluation and feedback-loop frameworks for agentic systems
- Familiarity with sandboxed code execution and agent harness development
- Experience with LLM observability and monitoring tooling and cost-governance / FinOps for AI infrastructure
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