Machine Learning Engineer

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 does both deep research and product development, not only building the frontier biological AI models in the field but also putting them in the hands of the researchers at the forefront of the life sciences. This fundamentally requires elite engineers and scientists working together to solve big research and product challenges. We are building a world-class multi-disciplinary team spanning AI research, engineering, biology research, and business roles, which requires strong communication and collaboration across roles.

The EvolutionaryScale team is based in two locations : 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 two 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 :

The salary range for this position is $150,000 to $350,000 per year, plus a competitive equity package. Compensation package will vary based on job-related skills, experience, and knowledge. The compensation package also includes comprehensive medical, dental, and vision benefits.

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