Staff Software Engineer (Life Sciences)

Join our AI Simulation group as a Staff Software Engineer, where you will be the primary technical bridge between our core Large Quantitative Models (LQMs) and our most ambitious client contracts. This role is designed for a resourceful generalist who thrives on autonomy and is passionate about tackling the hardest challenges in drug discovery and chemical simulation. You will work closely with internal scientific delivery teams and be responsible for the technical enablement and success of our most critical partnerships.

• Act as the primary technical bridge between core Large Quantitative Models (LQMs) and ambitious client contracts, ensuring technical enablement and success of critical partnerships.
• Partner with scientific teams to audit workflows, diagnose technical blockers, and take full accountability for the end-to-end technical execution of client contracts.
• Design and deploy high-velocity code to extend existing tools, or build new frameworks for wrapping scientific logic and automating complex R&D workflows.
• This role demands a "startup" mindset: the seniority to lead a project's technical vision and the humility to execute any task necessary to move the mission forward
• Proven ability to operate independently in high-ambiguity environments, moving from ideation to functional code with high velocity
• Hands-on experience with cloud orchestration (Kubernetes, Batch systems) and workflow management tools like Airflow
• Entrepreneurial mindset with a track record of shipping systems in fast-moving, customer-facing environments
• Technical expertise in building REST APIs, managing database systems (ORMs, schemas), and utilizing Infrastructure as Code (Terraform)
• 7+ years of professional software development experience, with deep proficiency in Python and modern architectural patterns
• Domain expertise in drug discovery, cheminformatics, or advanced materials
• Familiarity with High-Performance Computing (HPC), GPU programming, and optimization
• Experience delivering scientific code for R&D projects involving simulation or numerical optimization
• Exposure to AI applications involving knowledge graphs, AI agents, or multi-agent systems