Senior Director, Software Development, Test Automation

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