Senior Scientist – Research Engineering

Role Summary

We are seeking a Senior Scientist to lead our Research Engineering efforts and reimagine the digital infrastructure of drug discovery. You will be the technical lead behind the "Operating System" for ADMET Intelligence, bridging AI/ML, wet lab operations, and discovery projects to transform raw experimental data into predictive insights for scientists. Geography is flexible for this position.

Responsibilities

• Design and implement robust, scalable data foundations that streamline scientific data pipelines.
• Lead the design and deployment of modern, cloud-native applications (Python, FastAPI, React) that abstract complex AI predictions into intuitive interfaces, accelerating the Design-Make-Test cycle for bench scientists.
• Set the technical standard for the group: on code quality, CI/CD, and architecture.
• Partner with AI/ML researchers to deploy algorithmic solutions (SageMaker, LLMs) to production. You will translate high-level planning logic into executable API endpoints that integrate seamlessly with lab automation.
• Create automated workflows that ingest complex multi-modal data and deliver real-time insights to program leaders, drastically reducing the cycle time of discovery.
• Serve as the primary technical translator between AI/ML researchers, discovery teams, and IT/Cloud organizations, converting scientific intent into rigorous engineering specifications.

Qualifications

• Required: Bachelor’s Degree in Computer Science, Engineering, or scientific discipline with 5+ years of full stack development experience (or Master’s with 3+).
• Required: Experience building scalable production applications using Python (i.e. FastAPI) for backend and modern frameworks (i.e. React) for frontend.
• Required: Authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.

Skills

• Preferred: Direct experience wrapping AI/ML models into usable products (SageMaker endpoints, LLM tool-use, RAG architectures).
• Preferred: Strong ability to design and optimize data systems for high-throughput experiments.
• Preferred: Knowledge of AWS ecosystems (Lambda, S3, Step Functions, DynamoDB) and containerization (Docker, Kubernetes) in production environments.
• Preferred: Experience with modern data tools such as Databricks and experience handling complex, structured scientific or diverse datasets.
• Preferred: Proven ability to mentor junior scientists and influence technical decisions across cross-functional teams.
• Preferred: History of implementing robust CI/CD pipelines and Infrastructure-as-Code.
• Preferred: Experience working in Life Sciences, Biotech, or a similar R&D environment.