Staff Machine Learning Researcher - Biological Foundational Models
About Somite
Somite.ai is a venture-backed company aiming to become the OpenAI of stem cell biology , developing AI foundation models for human stem cells to drive novel cell therapies at scale.
We take a full-stack approach - generating data at 1,000x lower cost, training large-scale models with unmatched predictive power, and using these models to discover, refine, and optimize high-impact applications in cell therapy.
Our founding team includes repeat AI entrepreneur Dr. Micha Breakstone ( Chorus.ai , acquired for $575M), the Head of the Fundamental AI Group at MIT (pioneered the foundational research on scaling laws ), and three Harvard Medical School professors and National Academy of Sciences/Medicine members, including the Chair of the Genetics Department at HMS. Based in Boston and launching operations in January 2024, Somite has raised close to $60M to date ( Forbes ).
Somite’s Core Values:
We show up – fully accountable, all-in, doing whatever it takes
We act with urgency – swift, decisive, proactive
We support one another – collaborative, helpful, empathetic
Location:
Boston, MA (onsite, full-time)
About the Role:
As a Senior Machine Learning Researcher - Biological Foundational Models, you will play a key role in developing foundational models for single-cell RNA sequencing data. Working closely with other machine learning researchers and computational biologists, you’ll design cutting-edge AI solutions, contribute to pioneering research, and help build the core infrastructure driving Somite’s cell-replacement therapy platform. This role is ideal for a machine learning expert driven by scientific inquiry and eager to pioneer biological discoveries through foundational AI models.
Responsibilities:
Design, train, and optimize foundational models for single-cell RNA sequencing and other high-dimensional omics data
Collaborate closely with computational biologists to ensure models produce biologically meaningful, interpretable outputs, engaging deeply with biological questions and research
Design and implement novel deep learning architectures, including transformer-based models tailored to biological data
Build scalable, distributed pipelines for training and inference across trillion-token biological datasets
Provide mentorship and technical guidance to other machine learning researchers on the team
Qualifications:
6+ years of experience in machine learning, transformer-based deep learning, and large-scale data analysis, preferably in biological applications
Master’s degree or higher in Computer Science, Artificial Intelligence, Computational Biology, or a related field
Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
Proven experience building and optimizing large-scale models, including transformer-based architectures
Demonstrated track record of independent research and end-to-end model development, from prototyping to production
Passion for AI-driven biology and its potential to transform healthcare
Preferred Qualifications:
Strongly preferred: PhD with a strong publication record in top machine learning and/or computational biology journals
Strongly preferred: extensive background in biological research and biological modeling, ideally with single-cell RNA sequencing data
Experience with reinforcement learning (e.g., RLHF, PPO), diffusion models, or multi-modal architectures applied to structured, high-dimensional data
Experience in startups or fast-paced environments, with a self-directed, proactive work style
Benefits:
Take a technical leadership role with a mission-driven company with the potential to significantly impact the lives of millions
Work alongside a talented and passionate team at the forefront of AI and cellular biology
Contribute to the development of groundbreaking therapies that address significant unmet medical needs
Enjoy a competitive salary and benefits package, including flexible work options
Exceptional candidates who demonstrate outstanding capabilities and potential will be considered, even if they do not meet every qualification listed.
Join us and help unlock the full potential of AI for the benefit of human health!
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