Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley!Making medicine that's never been made means doing what's never been done. If you're an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation; we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us!
About the Lilly and NVIDIA PartnershipLilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley - an up-to-$1 billion, multi-year commitment to solve drug discovery's toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe.
What You'll Be DoingAs a Data Engineer, you will build and maintain the data platforms that power AI-driven research and discovery. You will develop scalable pipelines that ingest, transform, and deliver chemical, biological, and experimental data for machine learning and scientific workflows. Partnering with AI Scientists, AI Engineers, and laboratory researchers, you will ensure that data is accurate, traceable, and accessible at scale. Your work will provide the trusted data foundation behind next-generation AI models and experiments.
How You'll Succeed- Engineer datasets in large language environment for model training specifically efficient formats and storage layout (Parquet, Zarr, Arrow) and delivery fast enough that GPU clusters are never left waiting on data.
- Design, develop, and maintain scalable and efficient data pipelines to support data analytics, reporting, and machine learning initiatives.
- Ensure seamless data flow between systems and applications, optimizing data transfer and transformation processes for performance and scalability.
- Build the ingestion path from the automated lab, so experimental results reach the models in hours rather than weeks, closing the loop between what a model proposes and what the next model learns from.
- Own the correctness of what models train on completeness, sound joins across experimental sources, and validation that catches a bad dataset before it reaches a training run rather than after.
- Build dataset versioning, lineage, and reproducibility into the platform, so any model can be traced to the exact data it was trained on months or years later.
- Work with the laboratory, instrument, and external teams producing the data so that a change upstream does not quietly corrupt a training run downstream.
What You Should Bring- Strong Python, or equivalent experience building data-intensive software systems.
- Strong SQL and data modeling experience including designing schemas that hold up as scientific data grows and diversifies, with expert knowledge of Postgres or a comparable enterprise database.
- Distributed data processing (Spark, Ray, or Dask) and pipeline orchestration (Airflow or Dagster) at scale.
- Experience with cloud platforms - AWS and Azure preferred - and with high-performance and object storage feeding large-scale compute environments.
- A track record of building data systems that other people depend on, and of taking responsibility for them when they broke.
- Strong testing practices and test automation, with solid CI/CD and Git fundamentals.
- Adaptability and a collaborative mindset, with the ability to translate complex scientific questions into data solutions that accelerate experimentation and decision-making.
- Experience streaming and event-driven integration (Kafka, MQTT, or AMQP), including instrument and laboratory data capture.
- Cheminformatics or scientific data experience - compound registration, structure notation (SMILES, InChI, HELM), RDKit, multi-omics, assay, or sequencing data - is a strong plus.
- Prior experience across the following: data modeling, ETL/ELT at scale, ontology development, semantic graph construction and linked data, or relational schema design.
- Experience standing up, migrating, or consolidating databases and data platforms, including production cutover of systems in active use.
Your Basic Qualifications- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field.
- 5+ years of data engineering experience building and operating production data systems.
Location & Work FlexibilityThis role is based at our Silicon Valley Hub. We offer a flexible hybrid work model, with
three days onsite and two days working remotely each week, supporting both collaboration and work-life balance.
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$157,500 - $231,000
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
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