(Senior) Scientist, Multimodal Biological Reasoning

Flagship Pioneering, Inc.•Cambridge, MA

19h•$136,000 - $264,000

About The Position

Pioneering Intelligence is seeking a Senior Scientist for Flagship's ambitious efforts to build polyintelligent AI systems that unify human scientific expertise, machine intelligence, and nature’s biological signals into multi-modal, multi-scale reasoning engines for biology. As a Senior Scientist, you will have the unique opportunity to shape our biological reasoning efforts on both the technical and application levels. You will be a key contributor on a team innovating on machine learning model architectures that will create best-in-class biological reasoning engines that tackle the unconventional life sciences problems that Flagship pursues. You will work with teams in Pioneering Intelligence and Flagship-at-large to apply our reasoning engines to discover new biology and engineer new biological solutions through massively parallel in-silico reasoning.

Requirements

PhD in Computer Science, Machine Learning, Computational Biology, Systems Biology or related quantitative field (or Master’s with equivalent research experience).
Strong machine learning and software engineering fundamentals.
Experience handling and processing large scale biological datasets.
Ability to work independently in an ambiguous environment, foster cross-team collaborations, and communicate externally.
Demonstrated ability to work in cross-functional settings with scientists or biotech teams to bridge needs and technical implementation.

Nice To Haves

Experience pre-training, fine-tuning and/or post-training large foundation models (e.g. billion+ parameter LLMs or VLMs) or foundation models in biology (e.g. ESM, Evo2, Carbon, Nucleotide Transformer, UCE, CellFM, TranscriptFormer, etc.)
Experience with multi-modal machine learning, particularly non-text modalities.
High impact contributions in relevant venues, such as major product releases within a company, publications (NeurIPS, ICML, ICLR, Nature, Science, Cell, etc.), or open-source contributions in AI for Science.
Experience with causal reasoning, especially as relevant to perturbation biology, pathways, and/or mechanism-of-action reasoning
Fluency in at least one life science domain (molecular biology, genetics, biochemistry, cell biology, structural biology, biophysics, bioengineering, or related)
Experience deploying AI/ML into scientific workflows at enterprise scale.
Familiarity with distributed training infrastructure

Responsibilities

Design technical architecture, training strategies, and evaluations for a multi-modal, multi-scale biological reasoning model.
Train and validate large-scale biological reasoning models.
Development and curation of novel biological datasets. Construct new datasets and benchmarks to empirically evaluate biological validity and generalization.
Translate biological questions into well-defined ML problems, build and scale training data pipelines and model benchmarks for downstream applications.
Contribute to the technical and product roadmap, data strategy and research priorities.