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

Lead design and delivery of enterprise-grade platform capabilities: multi-tenancy, identity and authorization, tenant provisioning, metering/billing, audit/compliance, and automation. Act as a hands-on principal engineer across platform, security, and AI/ML teams to build scalable, secure, and operable systems for regulated scientific customers.

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.

As a Staff Software Engineer, you'll design and build high-performance APIs and UI, develop database architectures, and ensure system reliability while collaborating with scientists and ML engineers.

Own the Physical AI platform program: sequence roadmap delivery across teams, manage cross-team dependencies and risks, establish SLOs and launch readiness, coordinate releases, and raise operational rigor for reliability and scalability.

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.