Machine Learning Infrastructure Engineer
Genesis Molecular AI11 months ago
Remote, United States +2 moreSenior
Responsibilities
- Design and evolve the in-house workflow orchestration framework, including DAG construction, 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 eliminate bottlenecks involving I/O, serialization, recomputation, and parallelization.
- Develop lazy execution, incremental computation, intelligent caching, and artifact-reuse abstractions.
- Partner with ML researchers, computational chemists, and scientific software engineers to create scalable and reproducible computational pipelines.
- Improve the developer experience for authoring, debugging, monitoring, and extending distributed scientific workflows.
- Make architectural decisions involving local and distributed execution, storage, compute scheduling, data lineage, and reproducibility.
Requirements
- Strong systems engineering skills across APIs, distributed systems, storage, serialization, concurrency, resource scheduling, and performance.
- Hands-on experience building production pipelines or infrastructure that processes large datasets reliably and efficiently.
- Ability to work across workflow-framework and application layers and build pragmatic abstractions for specialized scientific workflows.
- Ability to collaborate closely with researchers and translate evolving 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.
- Nice to have experience designing workflow engines, schedulers, DAG execution systems, build systems, or dependency-driven computation frameworks.
- Nice to have experience optimizing scientific, ML, protein, cheminformatics, or computational biology data pipelines.
- Nice to have familiarity with Kubernetes, containerized compute, Terraform, cloud object storage, distributed compute, high-performance or GPU-based computing, and large-scale caching or data-lineage systems.
- Nice to have familiarity with molecular data formats and tools such as RDKit, OpenEye, BioPython, molecular dynamics tooling, or structural biology pipelines.
Benefits
- Competitive compensation package including salary and equity.
- Medical, dental, and vision benefits covered 100% for employees.
- 401(k) plan.
- Open unlimited PTO policy.
- Free lunches and dinners at the offices.
- Paid maternity and paternity leave.
- Life and long- and short-term disability insurance.
- The company is headquartered in San Mateo, California, with an integrated laboratory in San Diego, California.
Tech Stack
Categories
BackendData Engineering
About Genesis Molecular AI
Genesis Molecular AI develops GEMS, a generative and predictive molecular AI platform that designs and optimizes small‑molecule drug candidates, including the Pearl diffusion model for structure prediction. The company advances an internal discovery pipeline and partners with pharma on AI‑enabled discovery; recent collaborations include Gilead (2024) and Incyte (2025). It is privately held, headquartered in Burlingame, California, with a fully integrated laboratory in San Diego.