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Machine Learning Infrastructure Engineer

genesis-molecular-ai · San Mateo

New
🇬🇧 English
Apache Spark GCP Dataflow Apache Beam Airflow Dagster Flyte Ray Kubernetes Terraform Cloud object storage RDKit OpenEye BioPython Molecular dynamics tooling

Job description

About the role

Genesis Molecular AI is building the next generation of AI foundation models for drug discovery. As a Machine Learning Infrastructure Engineer you will create the data and orchestration systems that power large‑scale scientific workflows, bridging computational chemistry, structural biology and machine learning.

Key responsibilities

  • Design and evolve our in‑house workflow orchestration framework, including DAG construction, scheduling, caching, retries and observability.
  • Build and optimise large‑scale preprocessing pipelines for protein structures, chemical datasets, simulations and ML training data.
  • Profile end‑to‑end workflows and eliminate bottlene‑cks such as redundant I/O, serialization and unnecessary recomputation.
  • Develop abstractions for lazy execution, incremental computation and intelligent caching to minimise redundant work.
  • Collaborate with ML researchers, computational chemists and software engineers to translate scientific workflows into reproducible pipelines.

Required profile

  • Passionate about DAGs and dependency‑driven computation.
  • Impatient about latency and driven to accelerate slow pipelines.
  • Evangelist for lazy execution and caching, avoiding unnecessary recomputation.
  • Strong systems engineering background with experience in APIs, distributed systems and performance optimisation.
  • Hands‑on experience building production data‑intensive pipelines.
  • Ability to move between framework design and application‑level optimisation.

Required skills

  • Experience with workflow orchestration tools such as Airflow, Dagster, Flyte, Ray or similar.
  • Familiarity with large‑scale data processing frameworks like Apache Spark, GCP Dataflow or Apache Beam.
  • Kubernetes and containerised compute environments.
  • Infrastructure‑as‑code tools such as Terraform.
  • Cloud object storage and GPU‑based high‑performance computing.
  • Knowledge of molecular data formats and tools (RDKit, OpenEye, BioPython, molecular dynamics tooling).

What we offer

  • Competitive salary and equity package.
  • Comprehensive health benefits (medical, dental, vision) fully covered for employees.
  • 401(k) plan, unlimited PTO and paid family leave.
  • Free lunches and dinners at the office.
  • Life and short‑/long‑term disability insurance.

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Published 5 hours ago

Expires 1 month from now

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genesis-molecular-ai

San Mateo