Senior Director, Software Development, Test Automation (San Francisco)
Senior Director, Software Development, Test Automation Systems We’re hiring a Senior Director, Software Development, Test Automation Systems to architect and build Lila’s test automation platform and quality engineering practice for our AI‑powered scientific and lab automation products. 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 holds the bar across our SDLC. This is a builder‑leader role where you drive quality vision, write requirements, make build‑vs‑buy decisions, drive execution, and lead a small (3–5 person) team that delivers leverage. The operating model is federated: platform, standards, and metrics are your domain; engineering teams own test execution. 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—validation patterns, harnesses, and contracts that science operations teams operate day‑to‑day. Data integrity and ALCOA compliance are foundational to everything you build. What You’ll Do Architect and ship the test automation platform Design and build the test automation platform—frameworks, fixtures, golden datasets, test orchestration, and reporting—that the engineering org adopts by default Set standards across unit, integration, contract, end‑to‑end, regression, performance, and chaos testing for backend services, the frontend monorepo, and data pipelines Treat platform adoption, flake rate, and time‑to‑signal as first‑class engineering metrics Make build‑vs‑buy decisions with conviction Own the buy/build/borrow strategy across test infrastructure, eval platforms, browser/device clouds, observability, and lab QC tooling Justify every choice with TCO, signal quality, integration cost, and time‑to‑leverage—and revisit decisions as the org and tech landscape evolve Bias toward leverage: buy commodity capabilities, build differentiators (Lila‑specific AI evals, lab QC, scientific data integrity) Modernize CI/CD for fast, reliable signal Own the test execution layer of CI/CD: parallelization, caching, hermetic environments, ephemerally preview environments, 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 change‑failure rate, MTTR, test effectiveness, pipeline efficiency, coverage, and PR‑to‑prod lead time as outcomes Drive AI‑driven test automation 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 Validate every AI‑generated test through evals—no LLM‑authored test ships without proof it doesn’t degrade signal Establish the eval discipline for Lila’s AI/agent stack: golden datasets, rubrics, regression suites, offline online evaluation pipelines Define and operate the quality metrics system Define quality SLOs and adoption metrics by team and service: coverage, escape rate, MTTR, change‑failure rate, eval pass rate, lab QC violation rate Build dashboards that make quality visible from PR to executive review Apply Google SRE practices to prioritize where investment goes Mid‑long term – Stand up the QC framework for lab automation Design the validation framework, harnesses, and contracts that lab and Science Ops teams will operate Embed ALCOA principles: data integrity, audit trails, lineage from sample → instrument → output Partner with Research Ops on pre‑flight, in‑flight, and post‑flight validation patterns for autonomous lab execution Lead and coach across the engineering org Build a 3–5 person team of test automation engineers focused on platform leverage, not on writing tests for other teams Coach engineering teams on test design, quality investments, and adoption—make it cheaper to test well than to ship blind Translate UX and customer issues into testable contracts and platform improvements First 6–12 Month Outcomes First 90 days: Establish baselines—flake rate, time‑to‑signal, change‑failure rate, coverage, and current build‑vs‑buy footprint—and publish a quality scorecard with the first set of SLOs. Hire or onboard the initial 1–2 platform engineers. By 6 months: Ship v1 of the test automation platform adopted by at least one flagship engineering team by default; land CI/CD test‑execution improvements (parallelization, affected‑only selection, flake quarantine) with measurable time‑to‑signal reduction. Stand up the eval discipline (golden datasets, rubrics, regression suites) for the AI/agent stack. By 12 months: Drive default platform adoption across the engineering org; demonstrate AI‑driven test automation in production with eval‑gated rollout. Deliver the first operating version of the lab automation QC framework with ALCOA audit trails, validated end‑to‑end with Science Ops. Quality is visible from PR to executive review via live dashboards. Required Qualifications 10 years in software engineering, with 5 years leading test automation, quality engineering, or platform/SRE‑adjacent functions 3 years managing engineers, including building or scaling a team Strong software architect/engineer. You write designs your team wants to read and review. Python and/or TypeScript hands‑on expertise is highly desirable. Deep CI/CD expertise. GitHub Actions or equivalent at scale, monorepo build/test orchestration (Nx, Turborepo, or Bazel), test parallelization and caching, hermetic environments, ephemerally preview environments, flake quarantine, and test impact analysis Demonstrated build‑vs‑buy judgment. You’ve made and defended decisions on test infra, eval platforms, browser/device clouds, and observability — and can articulate the TCO and signal trade‑offs that drove them Hands‑on AI‑driven test automation experience. Using LLMs to generate, maintain, or triage tests in production, with rigorous eval validation. Fluency with eval frameworks 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 Metrics‑driven leader who drives outcomes through platform leverage and influence, not gatekeeping Customer‑ and UX‑first instincts: treats test automation as a vehicle for user experience, not a cost center Bonus Points For / 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 Compensation & Benefits Competitive base compensation with bonus potential and generous early‑stage equity. Expected base salary range: $260,000 USD – $390,000 USD. Full‑time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer‑paid life and disability insurance; flexible time off with generous company‑wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike‑share memberships for office‑based employees; and a company‑subsidized lunch program. Full‑time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.‑based positions; international salaries are set to local market. About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind’s greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard‑coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai . Equal Employment Opportunity Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy. Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the pro
