Machine Learning Engineer

EvolutionaryScale, now part of CZI Biohub, is pioneering the next era of AI-powered biology to understand and program life at scale. Together, we’re combining frontier artificial intelligence and frontier biology and building the technology to study how cells operate, organize, and work as part of systems to understand why disease happens and how to correct it. Our mission is to unlock new scientific discoveries that will help cure or prevent disease. Backed by the Chan Zuckerberg Initiative (CZI), Biohub operates as a nonprofit organization uniting leading scientists, engineers, and technologists across hubs based in California, New York, and Chicago.

In 2025, Biohub welcomed the EvolutionaryScale team - recognized for creating large-scale frontier AI systems for the life sciences - to accelerate the mission. Led by co-founder Alex Rives, who now serves as Head of Science at Biohub, the integrated organization is scaling up the compute and research infrastructure needed to build next-generation models of biology-powered by the largest cluster dedicated to open science.

We are at a pivotal moment in science offering a world-class interdisciplinary research environment across biology, data, and artificial intelligence and we will continue to hire for exceptional talent across these disciplines.

We are excited to strengthen in-person connections and cultivate a collaborative, team-oriented environment. This role is a hybrid position requiring you to be onsite for at least 60% of the working month, approximately 3 days a week.

• Contribute to the process of transitioning research to product by deploying models to production environments.
• Collaborate with researchers and engineers at the company’s partners (clients or academic partners) to deploy the company’s technology within their infrastructure. Be effective representatives of the company and our products to partners and other external collaborators.
• Manage artifact tracking for different fine-tuned models and data.
• Conduct data pipeline work including use of Apache Spark, Apache Arrow, Pandas, distributed computing for large-scale data processing.
• Help cultivate best practices in MLOps, and think about the full ML lifecycle, including data, fine-tuning, deployment, reliability and monitoring.
• Work on simplification and improvement of codebase abstractions to accelerate research momentum.
• Possess the ability to execute complex modifications to the research pipeline, such as fast data loading and distributed training.
• Handle DevOps responsibilities, focused on making all engineers and researchers more productive. This includes tasks like cluster monitoring, unit testing and integration testing of research codebase, and continuous integration.

Preferred qualifications

• Experience with Pytorch or low-level programming is a plus.
• Experience with distributed computing frameworks like Apache Spark or Dask.
• Knowledge of cloud computing platforms like AWS, Google Cloud, or Azure.
• Familiarity with containerization and orchestration tools such as Docker and Kubernetes.

The salary range for this position is $150,000 to $350,000+ per year. Compensation package will vary based on job-related skills, level of experience, and knowledge. 

We’re thankful to have an incredible team behind this work. To honor their commitment, we offer a wide range of benefits to support the people who make it all possible.

• Provides a generous employer match on employee 401(k) contributions to support planning for the future.
• Paid time off to volunteer at an organization of your choice.
• Funding for select family-forming benefits.
• Relocation support for employees who need assistance moving.

• Be part of a transformative scientific initiative backed by CZI’s long-term commitment and resources.
• Work alongside world-class researchers redefining how biology is understood and programmed.
• Access state-of-the-art compute infrastructure and cutting-edge lab technologies.
• Contribute to the first large-scale effort combining frontier AI with frontier biology to solve disease.
• Open Science that shares data, tools, and results to accelerate progress for everyone.