Lila Sciences
Senior Director, Software Development, Test Automation
San Francisco, CA USA · Director · Full-time
Sponsorship not specified$260k-$390kDetected 12 days ago
TypeScriptPythonBackend DevelopmentGitCI/CDGitHub ActionsSite Reliability EngineeringData EngineeringLLMsAI OrchestrationRecruitingLoad TestingTest AutomationResearchGxP
About the role
- You scale through tooling and influence.
- As you scale into this role, you'll also stand up the QC framework for our lab automation system - the validation patterns, harnesses, and contracts that science operations teams will operate day-to-day.
Responsibilities
- Design and build the test automation platform - frameworks, fixtures, golden datasets, test orchestration, and reporting - that the engineering org adopts by default
- Own the buy/build/borrow strategy across test infrastructure, eval platforms, browser/device clouds, observability, and lab QC tooling
- Make build-vs-buy decisions with conviction
- Bias toward leverage: buy commodity capabilities, build the differentiators (Lila-specific AI evals, lab QC, scientific data integrity)
- Own the test execution layer of CI/CD: parallelization, caching, hermetic environments, ephemeral preview envs, and affected-only test selection across our Nx monorepo/microservices.
- Build retry, quarantine, and impact-analysis systems so signal stays sharp as the org scales
- Drive AI-driven test automation
- Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges.
- Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.
- Reporting to the VP of Engineering, you'll own the test automation system, CI/CD test infrastructure, AI-driven test tooling, and the eval discipline that hold the bar across our SDLC.
Requirements
- 10+ years in software engineering, with 5+ years leading test automation, quality engineering, or platform/SRE-adjacent functions
- Python and/or Typescript hands on expertise is highly desirable.
- Deep CI/CD expertise.
- Hands-on AI-driven test automation experience.
- Track record of standing up a test automation platform that engineering teams adopted - not one bolted on
- Working knowledge of Google's SRE practices and a point of view on when they apply to pre-production quality
Nice to have
- Experience in GxP-regulated environments or scientific data integrity programs
- Experience with lab automation, LIMS, or other instrument-driven systems
- Multi-tenant SaaS quality at scale
- Exposure to event-driven systems, agent orchestration frameworks, or MCP
- Performance/load testing or chaos engineering background
- Your final offer will reflect your background, expertise, and expected impact.
- and a company subsidized lunch program.
- international salaries are set to local market.
Skills
- What You'll Be Building
- Apply Google SRE practices to prioritize where investment goes
Compensation
- We offer competitive base compensation with bonus potential and generous early-stage equity.
- USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.
- Expected Base Salary Range
- $260,000 - $390,000 USD
Benefits
- You will drive the quality vision, write requirements, make sharp build-vs-buy calls, drive execution, and build and lead a small (3-5 person) team that delivers leverage.
- Bonus Points For
- Drive change-failure rate, MTTR, Test effectiveness, pipeline efficiency, coverage, and PR-to-prod lead time as outcomes
- Apply LLMs across the full test lifecycle: test generation from specs and PRs, self-healing UI tests, synthesis, visual regression with vision models, and AI-assisted failure triage
- Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region.
Visa & Work Authorization
- al employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status
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This listing is sourced directly from Lila Sciences's careers page and normalized into a canonical job model.