Description
Pioneering Intelligence, a part of Flagship Pioneering, is seeking a Principal Scientist, Machine Learning - Multimodal Biological Reasoning.
As a Lead, you will have the unique opportunity to shape our biological reasoning efforts on both the technical and application levels. You will be a hands-on lead for a team of talented machine learning scientists innovating on model architectures that will create best-in-class biological reasoning engines.
Key Responsibilities:
- Own the roadmap: lead the project from architecture and data exploration through model readiness, benchmarking, refinement, and agentic integration.
- Guide technical architecture through a biological lens: guide multi-modal, multi-scale model design and ensure the system can ingest and reason over modalities relevant to mechanism-of-action reasoning, target discovery, perturbation biology, pathway reasoning, protein function, and autonomous discovery workflows.
- Translate biology into model and evaluation requirements: ensure model development is driven by biological relevance that enables real scientific workflows, not just benchmark performance or technical elegance.
- Build the engine for a novel science platform: develop and integrate reasoning engines into Pioneering Intelligence's AI Scientist platform for autonomous science.
- Source and shape portfolio use cases: interface with Flagship teams and portfolio company end-users to identify use cases, source data assets, define success criteria, and create end-user feedback loops for reasoning engines that are scientifically meaningful, tractable, and answer high-value questions with real world application.
- Communicate the impact: disseminate strategic direction and scientific results to Flagship stakeholders and in public forums.
Qualifications:
- Industry-leading expertise in modern LLMs, multimodal modeling, representation learning, fine-tuning, post-training, benchmarking, and ML systems.
- Deep experience in computational biology, AI-for-biology, AI-enabled drug discovery, translational data science, biological foundation models, or scientific discovery platforms.
- Demonstrated ability to work with scientists or biotech teams to scope high-value use cases, identify data assets, define success criteria, and translate discovery needs into technical execution plans.
- Working knowledge of biological data modalities such as genomics, transcriptomics, perturbation data, protein sequence, protein structure, pathways, imaging, pathology, or time-series biological data.
- Strong judgment around mechanism-of-action reasoning, target discovery, perturbation biology, scientific credibility, model limitations, hallucination risk, interpretability, and validation.
- Experience leading small, high-caliber technical teams through ambiguous scientific or product problems.
- Ability to communicate clearly with ML researchers, data engineers, portfolio-company scientists, executives, and venture creation leaders.
- Comfort operating in an entrepreneurial environment where the goal is not only to build a model, but to create a new capability that can reshape company creation and scientific discovery.
Requirements:
- PhD, MS, or equivalent experience in computational biology, machine learning, bioengineering, computer science, systems biology, quantitative biology, translational science, or a related field.
- Experience in biotech, pharma, AI-for-science, AI drug discovery, venture creation, or a platform organization serving multiple scientific programs.
- Experience deploying AI/ML for biology systems into scientific workflows at enterprise scale.
- Experience with LLMs, biological foundation models, protein language models, genomic foundation models, scientific agents, or AI discovery platforms.
- Experience working across multiple internal and external customers, therapeutic programs, or discovery teams.
Benefits:
- Healthcare coverage
- Annual incentive program
- Retirement benefits
- Broad range of other benefits