Jobs at Lila Sciences

The Senior Software Engineer will develop systems for scientific workflows, APIs, and applications, collaborating across teams to enhance performance and reliability in production environments.

The Staff Engineer will lead technical direction on data infrastructure, focusing on ingestion frameworks, storage architecture, and orchestration to support scientific workflows. Responsibilities include mentoring, ensuring reliability, and collaborating with cross-functional teams to establish coding standards and data integrity.

Lead the development of software infrastructure for protein design pipelines, ensuring reliable integration of ML systems and experimental workflows.

The Technical Program Manager will lead AI data generation projects, ensuring consistent quality and efficient execution while communicating across teams.

Lead the development and strategy of Lila Sciences' software products, focusing on integrating AI within scientific workflows and managing high-performing teams.

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

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.

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.

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

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.

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 Senior Software Engineer, you will design APIs, manage database architecture, optimize performance, and collaborate with cross-functional teams to support the Data Platform Team's backend solutions.

Design and implement high-performance UI and APIs, manage database architecture, develop applications, and ensure system scalability and optimization while collaborating with scientists and engineers.

As Commercial Counsel, you will draft and negotiate contracts, provide legal counsel on AI and science, and build scalable legal systems.

The Principal ML Engineer will design and scale ML infrastructure for scientific discovery, collaborating with AI scientists to enhance model training and deployment processes.

Lead the engineering team for Lila's data platform, focusing on architecture, delivery, and team management, while ensuring scalability and performance of data management systems.

Design and build integrations for AI-driven applications, develop databases, optimize performance, collaborate with scientists, and drive large-scale systems deployment.

As a Staff Software Engineer, you will develop UIs, APIs, and databases for an AI-driven scientific platform, ensuring performance and integration with various systems. Collaborate with researchers and engineers to enhance scientific workflows and scale large systems.

Lead and build a test automation platform and quality engineering practice for AI-driven scientific and lab automation products. Architect CI/CD test execution, AI-driven test tooling, eval pipelines, and lab QC frameworks (ALCOA+). Hire and lead a small platform team, set standards and metrics, drive platform adoption, and deliver measurable improvements in signal, flake rate, and time-to-production.