Senior Engineer - Drug Discovery Platform & Data Backbone

Lila Sciences; Cambridge (MA); On-site; USD 148,000 - 240,000; Full time

Benefits offered by this job

Commuter benefits

Lunch program

Educational assistance

Flexible PTO

Job summary

Lila Sciences in Cambridge, MA is hiring a Senior Engineer for the Drug Discovery Platform to own the data backbone enabling a closed loop from computation to experiment. You will design the molecule queue, registration system, and pipelines feeding back into AI models.

You will also build pipelines ingesting synthesis, QC, and assay data, maintain inventory constraints, and uphold data quality with strong SLAs for rapid experimentation.

Qualifications

• Bachelor's or Master's in CS, Chemistry, Computational Biology, or related field with 5+ years building production data platforms.
• Proficiency in Python and SQL and production-quality code.
• Experience with relational and/or NoSQL databases and schema design for evolving scientific data.
• Familiarity with lab data modeling (structures, assays, dose-response, ADMET).
• Experience with AWS and containerized deployment (Kubernetes).
• Experience with AI-assisted development tools and workflows.

Responsibilities

• Own the data backbone for the Drug Discovery Platform, including molecule queue and registration system.
• Build pipelines ingesting data from synthesis, QC instruments, and assays into a queryable store.
• Maintain synthesis constraints, inventory state, and lab integration interfaces.
• Ensure data quality, lineage, schema evolution, and SLAs for fast closed-loop cycles.

Skills

Data platform development

Python

SQL

Backend APIs

Data pipelines

AWS

Kubernetes

AI-assisted tools

Education

BSc/MSc in CS or related field

Tools

Flyte/Airflow/Dagster

Docker

RDKit/OpenEye (bonus)

Job description

Lila Sciences in Cambridge, MA is hiring a Senior Engineer for the Drug Discovery Platform to own the data backbone enabling a closed loop from computation to experiment. You will design the molecule queue, registration system, and pipelines feeding back into AI models.

You will also build pipelines ingesting synthesis, QC, and assay data, maintain inventory constraints, and uphold data quality with strong SLAs for rapid experimentation.