Machine Learning Research Engineer

Overview I am working with a leading Tech Bio company in Boston, looking for a Senior (Senior/Principal/Staff Scientist) Machine Learning Research Engineer to lead the development of their Biological AI Model.

The candidate should be local to Boston on a weekly basis.

Key Responsibilities

Design and implement core

AI/ML models

for simulating cellular systems using multi-omics and single-cell data.

Develop novel architectures

e.g.

Graph Neural Networks, Causal Inference, Transformers, diffusion models, VAE

etc .

tailorable to biological complexity.

Contribute to the

strategic direction of modeling efforts , helping define what to build, why, and how.

Lead model design from

prototyping to production

Guide internal thinking around

biological networks, perturbation models , and high-dimensional cellular data.

Support cross-functional collaboration and help define a scalable modeling stack and modeling best practices across the company.

Qualifications

MS or PhD in Computer Science, Physics, Applied Math, or similar , with a strong focus on AI/ML.

Strong track record in research outputs on single cell, AI method development.

Expertise in building models using

GNNs, VAEs, Transformers ,

reinforcement learning

or other deep learning approaches.

Strong proficiency in Python and deep learning frameworks such as PyTorch, TensorFlow or JAX.

Exposure to

single-cell data (e.g., scRNA-seq, spatial omics)

Strong ability to abstract and model

complex biological processes

from a data/physics/ML perspective.

Experience with

scaling models across biological levels

- from individual cells to tissues and whole organisms, is a strong plus, given the complexity of

multi-scale integration .

Experience working on

noisy, high-dimensional, multi-modal biological data sets .

Curious, collaborative, and comfortable in fast-moving, exploratory R&D environments.

Previous experience with Virtual Cell Models is a plus

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