About the job
Merge Labs is a pioneering research laboratory dedicated to uniting biological intelligence with artificial intelligence, aiming to enhance human potential, autonomy, and overall experience. We are innovating groundbreaking methods for brain-computer interfaces that facilitate high-bandwidth interactions with the brain, seamlessly integrate advanced AI, and ensure safety and accessibility for all users.
About the Team:
At Merge Labs, we are developing the future of brain-computer interfaces through the integration of cutting-edge advancements in synthetic biology, neuroscience, AI, and non-invasive imaging. Our cross-functional data and software engineering team collaborates closely with wet-lab scientists, automation engineers, and data scientists to construct a digital infrastructure that expedites molecular discoveries and optimizes device performance.
About the Role:
We are seeking a Senior / Principal ML Engineer to lead the development and ownership of the digital infrastructure supporting Merge's extensive computational operations. In this role, you will design distributed training and inference systems, experiment tracking, and deployment frameworks, empowering data scientists to swiftly iterate on models encompassing de-novo molecular design, biophysical modeling, signal processing, and computer vision. Your architectural contributions will transform research prototypes into production-ready systems, enhancing the speed, rigor, and fluidity of every computational scientist's workflow.
Key Responsibilities:
Develop the scientific and engineering framework for active learning and closed-loop optimization, including data ETL, machine learning modeling, and library architecture.
Work alongside computational scientists to establish achievable optimization goals and encode domain-specific knowledge and constraints.
Create model registries, evaluation frameworks, and automated reporting systems for benchmarking and experimental comparisons.
Implement CI/CD pipelines and resource orchestration using tools like Kubernetes, Ray, or Slurm.
Define and manage the ML engineering roadmap, providing mentorship to other computational scientists while establishing best practices for code quality, testing, and reproducibility.

