Machine Learning Engineer - Drug Discovery

Role Summary

We are seeking a Machine Learning Engineer who has experience working with small

molecule drug discovery datasets and can hit the ground running to deliver high-impact,

production-grade solutions to advance our programs. The ideal candidate will build and

scale data and ML infrastructure across early research, lead optimization, and development

phases of drug discovery pipeline. You will enable data-driven science while ensuring

robust engineering practices and FAIR data principles.

Key Responsibilities

• Support management of biobank scale datasets in Polaris, Maze’s internal platform

supporting Compass, by building scalable data ingestion, cleaning, processing, and

validation pipelines.

• Work with scientific compute teams to design and deploy machine learning models to

support workflows in research and small molecule drug discovery (compound property

prediction, assay data prediction, data analysis).

• Lead the evaluation and integration of Large Language Models (LLMs) to automate data

ingestion workflows, enhance intelligent querying, and support user-facing variant

association and scientific visualization platforms.

• Design and operate scalable ML and data platforms leveraging Terraform (IaC) and Git-

based CI/CD pipelines, incorporating workflow orchestration, automated model

lifecycle management, and production-grade monitoring and reliability.

• Collaborate with development organization to evaluate and deploy ML tools that

support workflows across Regulatory, Clinical Operations, and Medical Affairs.

• Collaborate cross-functionally translate scientific requirements into production-grade

Systems.

Required Qualifications

• Master’s degree in Computer Science, Machine Learning, Bioinformatics, Data

Engineering, or a related field.

• 3 years of industry experience building production-grade data and ML pipelines,

preferably in life sciences supporting drug discovery.

• Hands-on experience deploying AI/ML models in drug discovery applications (e.g.,

computational biology/chemistry workflows).

• Experience with FAIR data principles and strong programming skills in Python and SQL

(R is a plus).

• Proven experience in deploying and maintaining ML systems, including CI/CD,

workflow orchestration, and monitoring.

• Experience with workflow orchestration tools (e.g., Airflow, Prefect).
• Experience with containerization and cloud infrastructure (Docker, Kubernetes, AWS or

similar).