Jobs at Lila Sciences

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.

The role involves implementing AI safety strategies, building evaluation frameworks, and developing safeguards against scientific risks from AI models. Candidates must possess strong technical knowledge and communicate complex concepts effectively.

Build and operate enterprise developer platform features: multi-tenancy, authz/audit, tenant lifecycle and provisioning, public/streaming APIs and SDKs, and integrations for MCPs/agents. Ship backend services in Python/FastAPI, design tenant-aware schemas and isolation, support billing/metering, debug production issues, and collaborate across infra, data, apps, and science teams to meet regulated deployment requirements.

The role involves researching and developing training strategies for large language models, focusing on their applications in scientific reasoning and performance evaluation.

Design and maintain large-scale distributed training systems, optimize performance for ML models, and improve training efficiency.

As a Senior Software Engineer, focus on backend development and AI integration for scientific applications, building systems that leverage advanced AI techniques.

Lead sourcing, evaluating, and commercializing physical-science opportunities (materials, chemistry, energy). Build market and deal models, run primary/secondary research, define specs and commercial theses, and shape partnership/deal structures to launch ventures.

Design and build high-performance UI and APIs, manage database architecture, optimize systems for reliability and performance, and collaborate cross-functionally in a scientific context.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

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

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

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.

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.

The Senior/Principal Scientist will develop AI-driven workflows for antibody design and optimization, collaborating with biology teams to validate models experimentally.

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 multi-quarter AI platform programs across compute, storage, utilization, vendor partnerships, researcher support, and evaluation infrastructure. Coordinate cross-functional teams, define metrics and roadmaps, drive governance and adoption, and deliver measurable improvements in capacity, cost, observability, and researcher experience.

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