Scientific Lead - Forward Deployed AI Engineer, Applied Intelligence for Discovery
Eli Lilly and Company6 months ago
Base Salary
$167k - $266k/yr
Responsibilities
- Embed with computational biology and disease biology teams to understand their workflows, data, tools, and bottlenecks.
- Translate scientific use cases into prototypes, working demonstrations, evaluation benchmarks, and clear success criteria.
- Design, integrate, and ship production AI systems for drug discovery while owning data provenance, reliability, and on-call readiness.
- Apply LLMs, retrieval-augmented generation, text-to-SQL, agentic AI frameworks, and related approaches to target identification, biomarker prioritization, mechanism-of-action studies, and multi-omics analysis.
- Run workflow-specific evaluation loops, perform error analysis, and use results to guide model selection and product improvements.
- Distill deployment learnings into reusable primitives, reference architectures, validation templates, and benchmark harnesses.
- Partner with AI/LLMOps engineers to feed field-tested solutions back into the platform as reusable components.
- Drive adoption of technical tools among scientific and other non-engineering users.
Requirements
- PhD in computational biology, bioinformatics, data science, computer science, or a related field plus 3+ years of software/ML engineering or technical deployment experience; alternatively, an MS in a related field plus 5+ years of such experience, or equivalent demonstrated experience.
- Strong Python programming skills and familiarity with modern AI/ML ecosystems, LLM API usage, prompt engineering, and fine-tuning.
- Experience owning AI deployments from scoping through production adoption, including evaluation design, error analysis, and iterative evidence generation.
- Biological knowledge sufficient to work productively with computational scientists; prior multi-omics experience is strongly preferred.
- Experience building data-driven applications such as interactive dashboards, natural language interfaces, or automated analysis pipelines.
- Familiarity with cloud computing, preferably AWS, and Git version control.
- Experience in pharmaceutical, biotech, or life sciences research and development environments.
- Familiarity with agentic AI workflows that chain multiple models or tools.
- Experience with biological foundation models such as scGPT, Geneformer, ESM, or AlphaFold, or their application to research problems.
- Knowledge of biomedical ontologies, knowledge graphs, or heterogeneous biological data integration.
- Clear communication across scientific, computational, technical, and executive audiences.
- Open-source contributions or a public portfolio of applied AI work are preferred.
Benefits
- Full-time employees are eligible for a company-sponsored 401(k), pension, vacation benefits, medical, dental, vision, and prescription drug benefits.
- Additional benefits include flexible spending accounts, life insurance and death benefits, leave benefits, employee assistance, fitness benefits, and employee clubs and activities.