Senior Machine Learning Engineer, Ginkgo Bioworks

• PhD in a scientific discipline and a minimum of 5 years related experience; may include post-doctoral experience; Masters and 7 years of related experience; Bachelors and 9 years of related experience in data engineering, systems engineering, machine learning and operations, MLOps, or similar roles; or equivalent industry experience
• Deep experience with Python
• Experience with ML and data orchestration and workflow engines like Airflow, Kubeflow, Flyte, or Dagster
• Familiarity with recent literature and state of the art for large model architectures and training approaches
• Experience with building machine/deep learning models with at least one common framework such as PyTorch, Tensorflow, or JAX

Desirable

• As one of the first members of this new team, you will have a large role in establishing the core architecture, making technical choices, and creating patterns of our model training and serving infrastructure
• You will help create the data and model provenance and management infrastructure necessary for such a project
• You will work closely with experts in DNA design and Protein Engineering to enable scientists of various levels of exposure to modern ML to create foundation models for biology, train new task-specific models, and leverage existing ones
• Build, manage, and evolve a GCP-based platform for large scale (up to 100B+ parameters) training, evaluation, and serving of Foundation Models for biology
• Develop, implement and maintain a system for creating smaller models that combine large FMs with additional experimental data to address specific needs and applications
• Own processes for data ingestion, data prep, data and model provenance tracking, and various other data engineering and ML Ops activities
• Contribute to model design and experimentation
• Identify opportunities for application of AI and ML across the company, create prototypes, and contribute to overall prioritization and roadmap development for AI at Ginkgo