Machine Learning Infrastructure Engineer

About the Team

At Genesis Molecular AI, we're a tight-knit team of proven deep learning researchers, software engineers, and drug discovery pioneers. Our shared mission is nothing short of revolutionary: to forge the next generation of AI foundation models that unlock new therapies for patients with severe diseases.

We conduct fundamental research at the intersection of machine learning, physics, and computational chemistry, pushing the boundaries of each field.

You will work alongside machine learning researchers, computational scientists, and engineers to build the infrastructure that transforms massive, heterogeneous protein and chemical datasets into reliable, high-performance inputs for our models and drug discovery workflows.

We are looking for a Machine Learning Infrastructure Engineer to build the data and orchestration systems that power molecular AI g discovery creates unusual infrastructure challenges: our workflows combine computational chemistry, structural biology, machine learning, and large-scale data processing in ways that don't map neatly onto traditional ETL systems. To meet these requirements, we have built our own pipeline orchestration framework—and we want an engineer excited to push it much further.

This role is deliberately split between building the platform and using it. Roughly 50% of your time will be spent designing and improving our in-house orchestration framework: its DAG abstractions, execution engine, scheduling, caching, observability, and developer experience. The other 50% will be spent building and optimizing the protein, chemical, and ML data preprocessing pipelines that run on top of it.

You'll work across both layers, finding opportunities to eliminate unnecessary computation, reduce latency, introduce lazy evaluation and caching, and make complex scientific workflows fast, reproducible, and easy for researchers to use.

• 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.

• Passionate about DAGs. You naturally think about computation as graphs of dependencies and care deeply about how work is scheduled, parallelized, cached, retried, and recomputed.

• Impatient about latency. When a pipeline takes hours, your instinct is to understand exactly where the time went and systematically make it faster.

• An evangelist for lazy execution and caching. You dislike unnecessary work and look for principled ways to avoid recomputation, move less data, and reuse intermediate results.

• A strong systems engineer. You are comfortable reasoning across APIs, distributed systems, storage, serialization, concurrency, resource scheduling, and performance.

• Hands-on with data-intensive systems. You have built production pipelines or infrastructure that processes large datasets reliably and efficiently.

• Comfortable moving between…