Technical Lead - Software Developer, Data Foundry

Responsibilities

• Design, build, and maintain data processing pipelines for complex scientific datasets (chemical, biological, HTE, and automation-generated data), ensuring FAIR compliance and machine-actionability.
• Develop RESTful APIs and microservices providing unified programmatic access to LIMS, ELNs, instruments, data warehouses (Postgres, Redshift, Snowflake), and analytical databases.
• Support continuous improvement of LIMS and adjacent systems to meet evolving scientific workflows, security, and scalability standards.
• Build ML deployment pipelines—experiment tracking, model versioning (MLflow, W&B), containerized serving, monitoring, and automated retraining.
• Implement model observability: drift detection, performance alerting, and lifecycle management.
• Collaborate with Methods4Insight to operationalize cheminformatics, statistical, and AI/ML models as production APIs.
• Develop agent-ready APIs with structured error handling, audit trails, and monitoring supporting agent autonomy and human oversight.
• Contribute to MCP servers or similar frameworks exposing Data Foundry capabilities to AI agents.
• Build software enabling closed-loop experimentation: agents design, automation executes, data flows back, models update.
• Create integrations connecting lab automation equipment, scheduling systems, and instrument data streams to Data Foundry’s infrastructure with proper metadata and traceability.
• Build modular, reusable automation workflow components scientists can configure without writing code.
• Work directly with bench scientists to rapidly prototype custom applications, dashboards, and workflow tools to improve scientist’s experience and efficiency.
• Validate prototypes through iterative scientist feedback, then partner with Tech@Lilly to hand off for enterprise scaling with defined transition criteria and documentation.
• Build and operate cloud-native components (AWS, Azure, or GCP) supporting containerized workflows (Kubernetes/Docker), infrastructure-as-code, CI/CD, and workflow orchestration (Prefect, Airflow, Nextflow).
• Apply DevSecOps standards including security scanning, code review, and automated testing.

Requirements

• B.S./M.S/Phd. in Computer Science, Bioinformatics, Computational Biology, Cheminformatics, Chemistry, Biology, Biomedical Engineering, or related STEM field.
• BS (with 10+years), MS (with 5+ years) or Phd (1+ year) of scientific software development experience, with understanding of experimental data types and scientific workflows.
• Proficiency in Python and at least one additional language (Java, C#, Go, TypeScript, or Rust); strong SQL skills.
• Experience building RESTful APIs, data pipelines, and/or microservices for scientific or technical applications.

Qualifications

• Pharmaceutical or biotech research industry experience, particularly in discovery workflows for biology, chemistry, biochemistry or automation.
• MLOps tooling: experiment tracking (MLflow, W&B), model registries, model serving, monitoring/drift detection.
• Familiarity with cloud platforms (AWS, Azure, or GCP), containerization (Docker/Kubernetes), and Git.
• Strong communication skills with a track record of productive scientist collaboration.
• Exposure to AI agent infrastructure, MCP frameworks, or building APIs that AI/ML systems invoke programmatically.
• Experience integrating lab automation systems with digital platforms or AI-driven workflows.
• Data warehousing experience (Postgres, Redshift, BigQuery, Snowflake) and scientific data standards/ontologies.
• LIMS/ELN experience (e.g., Benchling) and laboratory instrument integration.
• Workflow orchestration (Prefect, Airflow, Nextflow, WDL), CI/CD, and Linux/bash scripting.
• Strong learning agility—willingness to step outside comfort zone and adopt new technologies to get the job done.