Scientific Software Developer at Lilly

Setup: Hybrid
Location: San Diego, California · San Francisco, California · +3
Type: Full-time

Description

Lilly is seeking Scientific Software Developers to join their Data Foundry team. This role involves designing, building, and maintaining data processing pipelines, developing RESTful APIs, and creating scientific prototyping tools. Developers will also focus on automation software integration with lab equipment and cloud infrastructure using DevSecOps principles. The ideal candidate will collaborate with scientists and contribute to accelerating molecule discovery.

Required Skills

Python
SQL
RESTful APIs
microservices
Cloud Infrastructure
DevSecOps
Kubernetes
Docker
CI/CD
Workflow Orchestration

Additional Information

Required Previous Experiences: scientific software development
Preferred Qualifications: Experience (or demonstrated aptitude at junior levels) building RESTful APIs, data pipelines, and/or microservices for scientific or technical applications.
Familiarity with cloud platforms (AWS, Azure, or GCP), containerization (Docker/Kubernetes), and Git.
Strong communication skills and interest to collaborate with scientists and multi-functional teams.
Pharmaceutical or biotech research industry experience, particularly in discovery workflows for biology, chemistry, or automation.
LIMS/ELN experience (e.g., Benchling) and laboratory instrument integration.
Experience integrating lab automation systems with digital platforms, including instrument control, robotic workflow orchestration, or scheduling systems (OPC-UA, serial/USB protocols, automation scheduling platforms).
Data warehousing experience (Postgres, Redshift, BigQuery, Snowflake) and scientific data standards/ontologies.
Hands-on experience with cheminformatics tools (RDKit, Schrödinger, MOE) or bioinformatics platforms (Biopython, Bioconductor, sequence analysis pipelines).
Experience with scientific computing libraries (SciPy, NumPy) for numerical methods, ODE solvers, optimization, or PK/PD modeling workflows.
Workflow orchestration (Prefect, Airflow, Nextflow, WDL) and CI/CD practices.
Strong learning agility—willingness to step outside comfort zone and adopt new technologies to get the job done.
Experience with C, C++, or other compiled languages for porting performance-critical scientific workflows; ability to profile and identify computational bottlenecks.