Machine Learning Engineer – Biological Foundation Models

Metric Bio; Boston (MA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Competitive salary

Work with top talent

Impact millions of lives

Job summary

A biotechnology firm is seeking a Machine Learning Engineer to design and optimize innovative models for biological data. The role emphasizes collaboration with computational biologists and aims to redefine cell therapy development. Candidates should have significant experience in ML and deep learning, a strong publication record, and expertise in Python and ML frameworks. This position offers competitive compensation and a chance to make impactful changes in the biotechnology field.

Qualifications

• 6+ years of experience in ML, deep learning, or foundation models.
• First-author publications in top-tier ML/biology journals.
• Strong skills in Python and experience with ML frameworks.
• Strong Python and ML framework engineering; production readiness.
• Background in single-cell or omics data is ideal, ML-first innovators who can learn biology.

Responsibilities

• Design and optimize foundation models for omics data.
• Build scalable distributed pipelines for training.
• Collaborate with biologists to ensure outputs are meaningful.
• Prototype and deploy novel architectures tailored to biological data.

Skills

Machine learning

Deep learning

Python

PyTorch/TensorFlow

Transformers

Generative models

TensorFlow

Production readiness

Education

PhD in a relevant field or equivalent experience

Tools

Python

PyTorch

TensorFlow

Job description

Machine Learning Engineer – Biological Foundation Models

Metric Bio has partnered with a venture-backed biotech at the intersection of AI and cell biology. This team is building foundation models on trillion-token scale biological datasets to reimagine how we create cell therapies.

This is a role for someone who doesn’t just apply existing methods but creates new ones; first-author researchers, system builders, and innovators who want their work to drive real therapeutic impact.

Responsibilities:

• Design and optimize foundation models for single-cell and multi-omics data, leveraging transformer and generative architectures.
• Build scalable distributed pipelines (multi-GPU training, trillion-token inference) to push biology into true foundation-scale.
• Collaborate closely with computational biologists and wet-lab teams, ensuring models produce interpretable, biologically meaningful outputs.
• Prototype and deploy novel architectures tailored to biological data, with the freedom to shape strategy and direction.

Requirements:

• First-author publications in top-tier ML/biology journals.
• 6+ years of experience in ML, deep learning, or foundation models (academic or industry).
• Proven expertise with transformers, diffusion, or generative models.
• Strong Python + PyTorch/TensorFlow engineering skills; ability to move from research prototype → production.
• Background in single-cell or omics data is ideal, but ML-first innovators who can quickly learn the biology are very welcome.
• Track record of innovation: new methods, impactful papers, or deployed ML systems.

What We Offer:

• Technical leadership opportunity at a mission-driven company that has recently secured over $50M in funding.
• Work alongside top talent at the cutting edge of AI x biology.
• Chance to impact millions of lives by redefining how cell therapies are developed.
• Competitive compensation and benefits, with an emphasis on urgency, collaboration, and innovation.