Director, Software Development, Test Automation Systems to architect

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.

• 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 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, Analysis Skills, Artificial Intelligence (AI), Automation Engineering, Automation System Development, Automation Systems, Automotive Automation, Caching, Candidate Sourcing, Cloud Computing, Coaching, Communication Skills, Continuous Deployment/Delivery, Continuous Integration, Data Management, Data Quality, Data Sets, Engineering Management, Equal Employment Opportunity (EEO), Establish Priorities, Failure Analysis, GitHub, GxP, Laboratory, Laboratory Automation, Laboratory Information Management System (LIMS), Laboratory Operations, Load Testing, MCP - Microsoft Certified Professional, Machine Tool, Medicine, Metrics, Microservices, Operating Systems, Operations Research, Performance Testing, Process Improvement, Public/Media/Press/Analyst Relations, Python Programming/Scripting Language, Quality Control, Quality Engineering, Quality Metrics, Recruiting/Staffing Agency, Reporting Dashboards, Requirements Management, Scientific Method, Scorecarding, Software Architecture, Software Development Lifecycle (SDLC), Software Engineering, Software Testing, Software as a Service (SaaS), System Architecture, System Validation, Test Automation, Test Design, Test Plan/Schedule, Test Strategy, Test Tools, Testing, Total Cost of Ownership, User Interface/Experience (UI/UX), Web Browsers

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. 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.

International Benefits. 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.

Expected Base Salary Range

$300,000 - $390,000USD

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.