# Senior Director, Software Development, Test Automation at Lila Sciences > United States · Director+ · QA / Test · open. Source: https://alderhq.app/jobs/greenhouse%3Alilasciences%3A4294875009 (read 2026-11-06). ## Facts (observed) - Company: [Lila Sciences](https://alderhq.app/companies/lila-sciences) - Location text: San Francisco, CA USA; San Francisco, CA - Where it can be done from (inferred): United States (evidence: "Location: San Francisco, CA USA; San Francisco, CA") - Posted: 2026-07-09 (119 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://job-boards.greenhouse.io/lilasciences/jobs/4294875009 - Skills listed (inferred): LLMs, Distributed systems ## Pay and sponsorship - Posted pay: $260k–$390k per year (from the listing's pay field) - Market band for similar posted roles (SF Bay Area, n 4): p25 $224k, median $248k, p75 $278k - H-1B filings at this company for similar roles, FY26: 5, median offered wage $115k ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance)) ## Description (the employer's text) ### 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/greenhouse%3Alilasciences%3A4294875009 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.