Senior Director, Software Development, Test Automation

Job description

Your Impact at LILA The Role 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 hold the bar across our SDLC. This is a builder-leader role. 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. The operating model is federated: you own the platform, standards, and metrics; 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 — the validation patterns, harnesses, and contracts that science operations teams will operate day-to-day. Data integrity and ALCOA+ compliance are foundational to everything you build. What You'll Be Building 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 the 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, 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 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. What You'll Need to Succeed 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, ephemeral preview envs, 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 LL