Tech Lead Life Sciences

Role:
Tech Lead Life Sciences or Computational Chemistry exposure

Long Term Contract

The candidate should be local (Boston) and visit client's office at least 3 times.

Our client is seeking a dedicated Tech Lead to build-out of an industrial-leading AI-enabled Virtual Screening (AI-VS) platform within Early Molecule Discovery (EMD). The Tech Lead will work in close partnership with the AI-VS team and Tech serving as the primary technical point of contact across internal teams and external vendors.

• Own end-to-end architecture design and implementation, with a deliberate focus on enabling the AI-VS team to flexibly add, update, and retire screening workflows with minimal friction.
• Lead all code development with accountability for scalability and cost efficiency across the platform lifecycle.
• Containerize pipelines for reproducible and portable deployment; rigorously manage library and tool versioning to ensure scientific reproducibility across environments.
• Maintain virtual screening-ready compound and structural libraries, keeping them current and fit for purpose.
• Manage GPU and CPU compute resources to optimize performance and cost.
• Own data storage architecture, ensuring reliable, secure, and efficient access to screening data and results.
• Build out the full infrastructure platform in alignment with the agreed-upon architecture.
• Serve as the primary technical and infrastructure point of contact for external vendor relationships and internal Research IT coordination, including database, HPC, and other platform teams.

• Demonstrated prior experience in a senior software engineering or infrastructure engineering role, with hands-on expertise in:
• Computational pipeline development and workflow orchestration
• HPC and cloud-based compute management, including GPU/CPU resource optimization
• Workflow management frameworks (e.g., Nextflow)
• Container technologies for scientific software deployment (e.g., Docker, Singularity/Apptainer)
• Data storage architecture and management in a research environment
• Strong software engineering fundamentals with emphasis on scalability, maintainability, and cost efficiency

• Familiarity with Computational chemistry tools such as Schrödinger Suite (Glide, FEP, Phase), Open Eye, or similar
• Experience with AI/ML model deployment in a drug discovery context
• Familiarity with cheminformatics libraries such as RDKit