SENIOR MACHINE LEARNING ENGINEER

Metric Bio; San Francisco (CA); Hybrid; USD 130,000 - 230,000; Full time

Job summary

Metric Bio is seeking a Senior Machine Learning Scientist to lead innovative model design in drug discovery, leveraging vast biological datasets. The role is ideal for those combining a passion for ML and biology, and who excel at translating complex datasets into actionable experimental insights. The position offers generous benefits, including unlimited PTO and a hybrid work model.

Benefits

Unlimited PTO

Health coverage for employees and dependents

Office setup budget

Qualifications

• Experience applying ML to biology or chemistry problems.
• Familiarity with multimodal and self-supervised learning techniques.
• Exposure to distributed ML methods.

Responsibilities

• Design and implement ML models integrating biological data.
• Stay updated on ML research to apply techniques to biological datasets.
• Collaborate with biologists and data engineers for experimental insights.

Skills

Machine Learning

Biological Data Analysis

Graph Neural Networks

Transformers

Diffusion Models

Education

PhD in Machine Learning, Biology, or related field

Job description

Direct message the job poster from Metric Bio

Recruitment Partner | Biotech R&D | Computational Bio & Chem

About the Role

This team is pioneering a new frontier in drug discovery by combining large-scale in vivo single-cell datasets with cutting-edge machine learning. Their mission is to accelerate the discovery of more effective, context-aware therapeutics by building foundation models trained on real patient biology.

As a Senior Machine Learning Scientist , youll help design and train next-generation foundation models of gene regulation using one of the world’s largest in vivo single-cell perturbation datasets. This role is ideal for someone who thrives at the intersection of ML innovation and biological application, and who brings a non-incremental, forward-thinking mindset to foundational modeling challenges.

Key Responsibilities

• Design and implement ML models that integrate multiple biological data modalities (e.g., chemical structure, protein sequences, scRNA-seq)
• Leverage architectures such as transformers, graph neural networks, diffusion models, or state-space models to build generalizable representations of biological function
• Stay on the cutting edge of ML research and rapidly apply novel techniques to massive-scale biological datasets
• Collaborate closely with interdisciplinary pods composed of biologists, data engineers, and software developers to translate ML insights into experimental action

Key Responsibilities

• Design and implement ML models that integrate multiple biological data modalities (e.g., chemical structure, protein sequences, scRNA-seq)
• Leverage architectures such as transformers, graph neural networks, diffusion models, or state-space models to build generalizable representations of biological function
• Stay on the cutting edge of ML research and rapidly apply novel techniques to massive-scale biological datasets
• Collaborate closely with interdisciplinary pods composed of biologists, data engineers, and software developers to translate ML insights into experimental action

Nice-to-Have Qualifications

• Experience applying ML to problems in biology or chemistry
• Familiarity with multimodal, contrastive, or self-supervised learning techniques
• Exposure to distributed ML methods (e.g., FSDP, MoE, flash attention, tensor parallelism)

Additional Information

• This is a hybrid role based in either the San Francisco Bay Area or Greater Toronto Area
• Includes generous benefits such as unlimited PTO, health coverage for employees and dependents, and a one-time office setup budget

This opportunity is perfect for someone looking to lead at the bleeding edge of ML-driven biological discovery.

Seniority level

Seniority level

Mid-Senior level

Employment type

Employment type

Full-time

Job function

Job function

Science

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Senior Machine Learning Engineer • San Francisco, CA, United States