Flagship Pioneering, Inc. - Principal Scientist, Machine Learning - Multimodal Biological Reasoning
Requirements
• 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. • 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. • Flagship Pioneering invents and builds platform companies, each with the potential for multiple products that transform human health, sustainability and beyond. Since its launch in 2000, Flagship has originated more than 100 companies. Many of these companies have addressed humanity’s most urgent challenges: vaccinating billions of people against COVID-19, curing intractable diseases, improving human health, preempting illness, and feeding the world by improving the resiliency and sustainability of agriculture. • Flagship has been recognized twice on FORTUNE’s “Change the World” list, an annual ranking of companies that have made a positive social and environmental impact through activities that are part of their core business strategies and has been twice named to Fast Company’s annual list of the World’s Most Innovative Companies. Learn more about Flagship at www.flagshippioneering.com. • At Flagship, we accept impossible missions to enable bigger leaps. Our core values guide us through uncertainty and toward lasting impact.
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.
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