Genesis Molecular AI
Machine Learning Infrastructure Engineer
San Mateo, CA +2 · Hybrid · Full-time · Posted Nov 24, 2025
- Location
- San Mateo, CA
New York, NY
Remote
About the job
Build and improve the in-house workflow orchestration platform and the large-scale scientific data pipelines that support molecular AI and drug discovery.
What you'll do
- Design and evolve orchestration capabilities including DAG execution, scheduling, caching, retries, observability, and distributed execution.
- Build and optimize protein, chemical, simulation, and machine learning data preprocessing pipelines; profile workflows and reduce bottlenecks.
- Collaborate with researchers and engineers to turn scientific workflows into scalable, reproducible pipelines and improve the platform and developer experience.
What they ask for
- Experience building production pipelines or infrastructure that processes large datasets reliably and efficiently.
- Strong systems engineering skills across APIs, distributed systems, storage, serialization, concurrency, resource scheduling, and performance.
- Ability to work across orchestration-framework and application layers and build pragmatic abstractions.
- Ability to collaborate with researchers and translate scientific workflows into robust infrastructure.
Nice to have
- Experience with large-scale ETL, data processing, or workflow systems such as Apache Spark, GCP Dataflow / Apache Beam, Flyte, Dagster, Airflow, or Ray.
- Experience with scientific or ML pipelines, Kubernetes, Terraform, cloud or GPU computing, caching and lineage systems, or molecular data tools.
Tools
Apache Spark, GCP Dataflow, Apache Beam, Flyte, Dagster, Airflow, Ray, Kubernetes
AI focus
Build infrastructure and preprocessing pipelines for molecular AI, machine learning, and drug discovery workflows.
Model-written summary · the original posting is authoritative