Research Engineer (Molecule Design Platform)

Key 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.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related field and 2+ years of industry experience
• Proficiency with Docker, Kubernetes, and PyTorch/PyTorch Lightning.
• Experience with molecular data (proteins, small molecules, or nucleotides)
• 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.
• Strong communication and collaboration skills in a fast-paced, interdisciplinary environment.

Preferred Qualifications

• 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.
• Understanding of biological data modalities or molecular representation learning is a plus.
• Industry experience deploying ML systems in production environments.