--- title: 'Senior Director, Software Development, Test Automation at Lila Sciences' canonical: 'https://feeny.ai/job/senior-director-software-development-test-automation-lila-sciences-san-francisco-p8yt670sgn8z' type: 'job' last_seen: '2026-09-11' --- # Senior Director, Software Development, Test Automation at Lila Sciences - **Company:** Lila Sciences - **Location:** San Francisco, CA - **Compensation:** $300k–$390k - **Posted:** 2026-07-09 - **Last confirmed live:** 2026-09-11 - **Apply:** https://job-boards.greenhouse.io/lilasciences/jobs/4294875009 ## 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 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 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—$390,000 USD ## 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. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In 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](https://www.lila.ai/candidate-privacy-policy-notice). A 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 property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto. ## About Lila Sciences ## Company Overview - **One-liner**: Lila Sciences is building the world’s first Scientific Superintelligence platform and autonomous lab, using AI to autonomously generate hypotheses, design and run experiments, and learn from results in real time across life sciences, chemistry, materials, energy, and defense. - **Entity Type**: Private (Privately Held) – Series A, Seed, and Grant funding rounds - **Headquarters**: Cambridge, Massachusetts, United States - **Founded**: Not publicly available (first funding round was Seed in March 2025) - **Founders**: Not publicly available (founded within Flagship Pioneering’s ecosystem) ## Core Business - **Primary industry**: AI-powered scientific discovery and autonomous laboratory platforms - **Target customers**: B2B – research organizations, biotech, pharmaceutical, energy, materials, aerospace, and defense companies - **Mission**: “Scientific Superintelligence to solve humankind’s greatest challenges” – accelerating discovery across medicine, materials, energy, and defense ## Products & Services - **LILA Platform (Scientific Superintelligence)**: An AI model and operating system that autonomously executes the entire scientific method – generating hypotheses, designing experiments, running them in physical labs, and learning from results in real time. - **AI Science Factory™ Instruments**: Proprietary hardware and robotics that serve as the “body” of the platform, enabling automated experimentation at scale. - **Domain-specific applications**: Tailored solutions for biotech (drug discovery, protein engineering), therapeutics (mRNA, antibodies, cell therapies), energy & environment (clean fuels, catalysis, critical minerals), advanced materials, chemicals, aerospace & defense, and oil & gas. ## Market Standing - **Valuation**: Not disclosed - **Key Metric**: Total funding – USD $550.7M (as of LinkedIn data) - Seed Round (March 2025): $200M led by Flagship Pioneering - Series A (September 2025): $235M led by Braidwell and Collective Global Management - Series A (November 2025): $115M led by NVentures (NVIDIA) - Grant (January 2026): $671,400 led by ARIA - **Notable Investors/Partners**: Flagship Pioneering, NVIDIA (NVentures), Braidwell, Collective Global Management, ARIA - **Growth Signals**: Named #25 on the 2026 CNBC Disruptor 50 List; headcount of 308 employees (monthly growth +10.6%); 122 active job postings; operates in 6 countries (US, UK, Canada, Poland, Spain, Germany) ## Competitive Advantages - **Proprietary AI model** that consistently outperforms other models across scientific domains in complex analysis and reasoning. - **Autonomous physical labs** that close the loop between AI hypothesis generation and real-world experimentation. - **“Team of Teams” operating model** enabling startup speed at scale while maintaining radical transparency and high trust. - **General platform approach** (inspired by Rich Sutton’s “Bitter Lesson”) rather than narrow domain-specific tools, allowing broad applicability. ## Strategic Focus - Accelerating discovery across medicine, materials, energy, and defense - Scaling the autonomous science platform to more industries and use cases - Building “Scientific Superintelligence” that can tackle humanity’s greatest challenges - Continued investment in AI research, robotics, and lab automation ## Why Work Here - **Culture**: Emphasizes velocity, trust, curiosity, truth, and grit. “Think freely, prove precisely.” A high-trust, mission-driven environment where scientists and engineers work side by side. - **Remote/Hybrid/Office**: Roles are listed in Cambridge, MA; San Francisco, CA; and London, UK. Physical lab presence suggests significant on-site work, but some roles may offer flexibility. Policy not explicitly stated. - **Notable perks/engineering culture**: Opportunity to work at the frontier of AI and scientific discovery; collaboration with world-renowned experts; access to cutting-edge robotics and AI infrastructure; strong emphasis on learning and teaching (“generous teachers and eager learners”). ## Sources 1. [lila.ai](https://www.lila.ai/) 2. [lila.ai/about](https://www.lila.ai/about) 3. [lila.ai/open-roles](https://www.lila.ai/open-roles) 4. [LinkedIn - Lila Sciences](https://www.linkedin.com/company/lila-sciences) 5. [Greenhouse Job Board](https://job-boards.greenhouse.io/lilasciences/jobs/4246302009) ## Other roles at Lila Sciences - [ML Engineer, Applied AI](https://feeny.ai/job/ml-engineer-applied-ai-lila-sciences-cambridge-bxk3xbqk91wd) — Cambridge, MA / San Francisco, CA - [Engineer I, Research Operations (2nd Shift)](https://feeny.ai/job/engineer-i-research-operations-2nd-shift-lila-sciences-cambridge-8ete74c2jp45) — Cambridge, MA - [Data Scientist II / Senior Data Scientist, Life Sciences](https://feeny.ai/job/data-scientist-ii-senior-data-scientist-life-sciences-lila-sciences-cambridge-wvrt5y3pkr50) — Cambridge, MA - [Senior Data Engineer, Bioinformatics, Cheminformatics, Materials](https://feeny.ai/job/senior-data-engineer-bioinformatics-cheminformatics-materials-lila-sciences-san-kp5d4nbn38vz) — San Francisco, CA - [Associate Director, App](https://feeny.ai/job/associate-director-app-lila-sciences-cambridge-46qgd5hp866e) — Cambridge, MA / San Francisco, CA - [Maintenance Engineering Technician II](https://feeny.ai/job/maintenance-engineering-technician-ii-lila-sciences-cambridge-qgp4st6zk9gw) — Cambridge, MA - [Research Scientist, Photonic Materials Discovery](https://feeny.ai/job/research-scientist-photonic-materials-discovery-lila-sciences-cambridge-m87b6zgatgmg) — Cambridge, MA - [Research Scientist I/II, Computational Organic Electronics](https://feeny.ai/job/research-scientist-i-ii-computational-organic-electronics-lila-sciences-mh2tq43ahp5d) — Cambridge, MA - [Associate Engineer/ Engineer I, Formulations and Characterization](https://feeny.ai/job/associate-engineer-engineer-i-formulations-and-characterization-lila-sciences-6dy4t3wbbqar) — Cambridge, MA - [Senior Manager, Scientific Discovery Capacity Planning](https://feeny.ai/job/senior-manager-scientific-discovery-capacity-planning-lila-sciences-cambridge-c1tjq0yzrj9f) — Cambridge, MA