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Seek an Enterprise Account Lead to drive revenue in life sciences by managing pharma accounts, leading sales cycles, and collaborating with technical teams to close deals.

The Principal Software Engineer will design and build APIs, optimize database architecture, enhance performance, and collaborate on cloud infrastructure for scientific applications.

This role invites interested candidates to join a talent community for future job opportunities at Lila.

Lead the development and integration of advanced safety systems for scientific AI applications, building a team and establishing a research strategy.

The Director/Senior Director leads new product planning and asset strategy for Lila Sciences, evaluating life sciences opportunities and shaping partnerships based on scientific and market insights.

As a Product Lead for Software/Applied AI, you will own specific product areas, guiding full-stack product execution and cross-functional collaboration to build impactful scientific tools.

Own and prioritize the capability roadmap for Lila's foundational models. Translate stakeholder input into measurable model requirements, define evaluation metrics and release gates, analyze competitor models, and partner with research and training leads to sequence and promote models to production with transparent documentation.

The AI Lab Research Engineer will develop scientific AI models for complex problems, design and deploy AI agents, and collaborate across disciplines in a lab environment.

The role involves designing, building, and optimizing backend systems and data infrastructure for orchestration algorithms and lab execution. Collaboration with robotics scientists and platform engineers is key to developing performant systems for AI-driven scientific platforms.

Lead verification & validation and software quality for autonomous lab robotics. Build Python test automation (unit, API, UI), hardware-in-the-loop and simulation-based tests, integrate suites into CI/CD, define V&V/test plans mapping requirements to coverage, diagnose nondeterminism/flakiness, and manage reproducible test data and fixtures across software-hardware boundaries.