Senior Machine Learning Engineer

Metric Bio; San Francisco (CA); Hybrid; USD 130,000 - 242,000; Full time

Job summary

A pioneering biotech company, Metric Bio, is seeking a Senior Machine Learning Scientist. This role calls for an innovative individual with expertise in ML and biology to help design next-generation models for drug discovery. The candidate will work with large datasets and collaborate with expert teams, playing a vital role in advancing therapeutics through cutting-edge research. The position includes generous benefits and offers a hybrid work environment.

Benefits

Unlimited PTO

Health coverage for employees and dependents

One-time office setup budget

Qualifications

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

Responsibilities

• Design and implement ML models for integrating biological data.
• Leverage architectures to build representations of biological functions.
• Collaborate with interdisciplinary teams to apply ML insights.

Skills

Machine Learning

Biology

Data Integration

Neural Networks

Transformers

Education

PhD in Machine Learning, Biology, or related field

Tools

TensorFlow

PyTorch

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, you'll 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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