EvolutionaryScale’s mission is to develop artificial intelligence to understand biology for the benefit of human health and society, through open, safe, and responsible research, and in partnership with the scientific community. Over the next ten years AI will transform biological design, making molecules and entire cells programmable. We will develop the foundation models for biology that enable this.

To continue to move the field forward in this emerging area, we prioritize individuals who have shown excellence and creativity in their respective domains over specific domain expertise. Having both biology and AI expertise is great, but not a requirement.

Our team bridges the gap between deep research and product development. We work together as a team to solve big research challenges, and value the ability to communicate and collaborate well. Research Engineers and Researcher Scientists are both researchers, and we assign equal value to both roles without hierarchy between them. We expect our technical staff to contribute to both research and engineering, while team members can sometimes be stronger at one or the other.

The EvolutionaryScale team is based in San Francisco and New York. We believe in flexibility around work schedules and locations, but expect that our team members will work half of the days or more of most weeks from one of our offices.

The Role

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

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Machine Learning Engineer

The Role

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