Onebridge, a Marlabs Company, is an AI and data analytics consulting firm that strives to improve outcomes for the people we serve through data and technology. We have served some of the largest healthcare, life sciences, manufacturing, financial services, and government entities in the U.S. since 2005. We have an exciting opportunity for a highly skilled Machine Learning Engineer (Biologics) to join an innovative and dynamic group of professionals at a company rated among the top “Best Places to Work” in Indianapolis since 2015.

This role is hybrid and requires an onsite presence 3 days per week. Candidates should be based in San Diego, CA.

For California residents, actual compensation will depend on the candidate's education, experience, skills, and geographic location. The anticipated salary range for this position is $160,000 to $200,000.

Machine Learning Engineer (Biologics) | About You

As a Machine Learning Engineer (Biologics), you are responsible for advancing molecular property prediction by integrating and extending state-of-the-art geometric graph and language model architectures. You will build a scalable and modular codebase that unifies the latest advancements across diverse chemical modalities such as small molecules, peptides, and antibodies. You excel at rigorously benchmarking models and automating hyperparameter tuning workflows to drive performance improvements. Your expertise in containerization, Kubernetes, and MLflow ensures efficient deployment and scalable production environments. You thrive in collaborative, interdisciplinary teams and are passionate about pushing the boundaries of molecular machine learning.

Machine Learning Engineer (Biologics) | Day-to-Day

• Integrate and extend advanced graph neural networks and transformer-based language models for molecular representation learning.
• Benchmark model performance on curated datasets using rigorous evaluation protocols and automated hyperparameter tuning workflows.
• Develop and maintain a robust, modular codebase that supports multiple chemical modalities and multimodal fusion of language and structural representations.
• Containerize ML workflows and manage distributed inference pipelines with Kubernetes for efficient use of GPU and CPU resources.
• Implement experiment tracking, model versioning, and deployment lifecycle management with MLflow, supporting cloud and on-premises deployment and monitoring.
• Collaborate closely with computational chemists, structural biologists, and data scientists to refine requirements, validate results, and document workflows for knowledge transfer.

**Machine Learning Engineer (Biologics) | Skills & Experience **

• 5+ years of experience in software engineering or AI/ML development, with at least 3 years focused on molecular machine learning and model integration.
• Proficiency in Python, PyTorch, and PyTorch Geometric, with experience applying deep learning to molecular and protein data.
• Expertise in graph neural networks (including SE(3) equivariant and invariant feature models) and transformer-based language models for molecular property prediction.
• Experience with multimodal model fusion techniques combining graph and language representations.
• Practical experience with Kubernetes for orchestrating ML workflows and MLflow for experiment tracking, model versioning, and lifecycle management.
• Strong knowledge of containerization (Docker) and cloud platforms (AWS, GCP, or Azure), with experience in distributed training and hyperparameter tuning frameworks (e.g., Ray Tune).

A Best Place to Work in Indiana since 2015