iO Associates are delighted to be partnered with this cutting-edge venture-backed Biotech at the forefront of combining AI and biology. They are seeking a skilled individual to join their team as a Staff Machine Learning Researcher. Located in Boston, our client is revolutionizing health technology with their innovative platform, aimed at engineering therapies for various diseases like diabetes, neurodegenerative diseases, and muscular dystrophies.

As a Staff Machine Learning Researcher, you will be pivotal in developing foundational models for single-cell RNA sequencing data. Collaborating with a team of experienced machine learning researchers and computational biologists, you will design advanced AI solutions to drive their therapy platform forward.

If you are a passionate machine learning expert eager to explore biological discoveries through AI models, this role is perfect for you.

Responsibilities:

Design, train, and optimize foundational models for single-cell RNA sequencing and other high-dimensional omics data
Collaborate with computational biologists to ensure models generate biologically meaningful outputs
Design and implement innovative deep learning architectures tailored to biological data
Build scalable pipelines for training and inference across vast biological data sets
Provide mentoring and technical guidance to fellow machine learning researchers

Essential Skills & Experience:

6+ years of machine learning experience, preferably in biological applications
Master's degree or higher in Computer Science, Artificial Intelligence, or related field
Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow
Track record of building and optimizing large-scale models, including transformer-based architectures
Passion for AI-driven biology and health care transformation

Desirable Skills & Experience:

PhD with a strong publication track record in machine learning or computational biology journals
Background in biological research and modeling, particularly in single-cell RNA sequencing
Familiarity with reinforcement learning, diffusion models, or multi-modal architectures applied to high-dimensional data
Experience in fast-paced environments or start up settings

Applicant must be based or commutable to Boston.