Machine Learning Infrastructure Engineer

Genesis Molecular AI develops AI foundation models for molecular discovery and drug design. The Machine Learning Infrastructure Engineer will build and evolve workflow orchestration systems and optimize large-scale protein, chemical, simulation, and machine learning data pipelines. The role focuses on distributed execution, caching, performance, reproducibility, and developer experience for scientific workflows.

Responsibilities:

• Design and evolve our in-house workflow orchestration framework, including DAG construction and execution, dependency management, scheduling, caching, retries, observability, and distributed execution
• Build and optimize large-scale preprocessing pipelines for protein structures, chemical datasets, simulations, and machine learning training data
• Profile end-to-end workflows and aggressively eliminate bottlenecks—from redundant I/O and serialization to unnecessary recomputation and poorly parallelized workloads
• Develop abstractions for lazy execution, incremental computation, intelligent caching, and artifact reuse so expensive scientific computations happen only when necessary
• Partner closely with ML researchers, computational chemists, and scientific software engineers to translate complex research workflows into scalable, reproducible computational pipelines
• Improve the developer experience for scientists and engineers authoring pipelines, making sophisticated distributed workflows intuitive to define, debug, monitor, and extend
• Make architectural decisions spanning local and distributed execution, storage, compute scheduling, data lineage, and reproducibility
• Use lessons from real-world scientific pipelines to continuously improve the orchestration platform itself—and use improvements to the platform to unlock faster, more ambitious scientific workflows