GenBio AI is looking to transform the landscape of biology and medicine through the power of Generative AI, aiming to decode biology holistically and enable the next generation of life-transforming solutions by pioneering a new era of biomedicine with foundation model training.
Requirements
- Proficiency with Docker, Kubernetes, and PyTorch/PyTorch Lightning.
- Strong software engineering foundations, including version control, testing, and code quality practices.
- Hands-on experience developing and deploying APIs for ML inference.
- Experience scaling distributed training or inference pipelines in production.
- Experience with orchestration and CI/CD tools such as Ray, Kubeflow, or ArgoCD.
- Familiarity with GraphQL, RESTful API design, and cloud infrastructure (AWS, GCP, or OCI).
- Prior experience optimizing inference code for large-scale models or biological data.
Responsibilities
- Design, develop, and optimize machine learning inference and training pipelines for molecular and biological data.
- Implement and execute large-scale hyperparameter searches to optimize model performance across molecule design tasks.
- Productionize ML models including packaging, containerization, and scalable deployment.
- Build, deploy, and maintain APIs and services for model inference and integration with downstream tools and data systems.
- Ensure scalability, observability, and reproducibility across all ML workflows.
- Collaborate closely with research scientists and data engineers to translate model prototypes into reliable production systems.
- Maintain high engineering standards through testing, documentation, and CI/CD practices.
Other
- Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field and 2+ years of industry experience
- Strong communication and collaboration skills in a fast-paced, interdisciplinary environment.
- Understanding of biological data modalities or molecular representation learning is a plus.
- Industry experience deploying ML systems in production environments.
