Senior / Principal Machine Learning Infrastructure Engineer - Foundational Models
Somite AIAbout the role
About Somite.ai
Somite.ai is a venture-backed company transforming stem cell biology with AI. We recently raised over $47 million in a Series A funding round, bringing our total funding to about $60 million. Just as LLMs revolutionized human language, we’re decoding the language of cells—how they communicate and decide their fate—using vast amounts of in-house generated data. Our AI models enable precise control over cell behavior, unlocking the potential to engineer therapies for diseases like diabetes, neurodegenerative diseases, and muscular dystrophies. Our platform, DeltaStem, accelerates therapy creation and continually improves through data-driven insights.
Founded by Dr. Micha Breakstone, a serial AI entrepreneur from MIT, and three Harvard Medical School professors, including the Chair of Genetics, Somite.ai is at the forefront of a new era in healthcare.
Location: Boston
Click the following links to learn more about Somite: https://x.com/SomiteAi/status/1922284999891472808
Click the following link to Apply:
https://jobs.ashbyhq.com/somite-ai?utm_source=JExzWMQvdD
About the Role:
As a Senior Machine Learning Engineer – Foundational Models, you will play a key role in building large-scale models for single-cell RNA sequencing data, trained on trillion-token datasets. Collaborating closely with machine learning researchers, computational biologists, and software engineers, you’ll design cutting-edge AI solutions, contribute to novel scientific research, and help develop the core infrastructure powering Somite’s cell-replacement therapy platform. This role is ideal for someone passionate about applying LLM-scale machine learning to complex biological challenges.
Responsibilities:
● Lead the development of infrastructure for training trillion-token, multi-billion parameter foundational models
● Design, train, and optimize foundational models for single-cell RNA-seq and other high-dimensional omics data
● Build scalable distributed systems for model training and inference on trillion-token biological datasets
● Develop MLOps pipelines and tools for continuous training, hyperparameter tuning, and deployment
● Design and implement novel deep learning architectures, including transformer-based models tailored to biological data
Qualifications:
● 7+ years of experience in machine learning, deep learning, and large-scale data analysis, ideally in biological applications
● 2+ years of experience training large-scale foundational models, including hands-on use of tools like DeepSpeed and model/data parallelism strategies
● 2+ years of experience applying ML in multi-node, multi-GPU, cloud-based environments
● 3+ years of professional software engineering experience in an industry setting.
● Very strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
● 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:
● Background in biological research and modeling, ideally involving single-cell RNA sequencing data
● 2+ years of experience with ML Ops and full model lifecycle development, using tools like MLflow
● Significant Full-stack software development experience
● Experience in startups or fast-paced environments, with a self-directed, proactive work style.
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