We are an innovative technology company providing a cutting-edge, AI-powered cloud platform for the construction industry. Created by industry experts with deep estimating experience, our software dramatically streamlines the pre-construction process. Our solution uses advanced machine learning to automate traditionally time-consuming takeoff tasks, helping estimators work up to 80% faster while reducing costly errors.
Our collaborative platform enables real-time teamwork, instant drawing analysis, and features a revolutionary conversational AI interface that transforms how professionals interact with construction plans. Founded by construction industry veterans, our award-winning application automates the takeoff process, enabling estimators to analyze blueprints in seconds rather than hours or days.
What you’ll do
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Own quality strategy end-to-end: define, implement, and continuously evolve testing standards across functional, non-functional, and AI-specific dimensions , ensuring quality is embedded from requirements through production.
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Build and maintain non-functional test automation: design and run performance, load, and stress test suites (k6, JMeter, Gatling etc.) integrated directly into CI/CD pipelines, with quality gates that protect every release.
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Design and operate (or contribute to) LLM/AI eval frameworks: establish evaluation pipelines (using tools such as DeepEval, Langfuse etc.) to assess AI feature quality across metrics including accuracy, hallucination rate, relevance, faithfulness, and safety.
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Test AI features and agentic behaviours: validate non-deterministic outputs, prompt variability, model regression, guardrail enforcement, and multi-step agent task-completion rates as first-class quality concerns.
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Champion shift-left and continuous testing: embed QA into planning, design review, and sprint ceremonies so defects are caught before they're coded, not after they ship.
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Drive a quality engineering culture: act as a quality advocate across engineering, product, and AI teams; run blameless post-mortems, define quality metrics, and make test coverage and reliability visible to the whole organization.
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Accelerate delivery through AI-assisted tooling: use AI coding assistants, self-healing automation, and intelligent test prioritization to increase the leverage of every hour spent on quality work.
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Build observability into production: define and monitor post-release quality signals, model drift indicators, and SLO thresholds so the team can distinguish a regression from expected non-determinism.
What you bring
Must-Haves
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Traditional QA foundations: solid understanding of deterministic testing: test planning, test case design, functional/regression/exploratory testing, defect lifecycle management, and quality metrics.
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Test automation engineering: deep expertise in writing and maintaining automated test suites using modern frameworks (Playwright, Cypress, or similar) with at least one modern programming language, such as TypeScript (strongly preferred) or Python , specifically for building robust test libraries.
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Non-functional test automation: hands-on experience designing and running performance, load, and stress tests with tools such as k6 or JMeter, including CI/CD integration and threshold-based quality gates.
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AI/LLM testing literacy: practical understanding of what makes AI systems non-deterministic, and experience (or strong working knowledge) of testing LLM-based features for hallucination, consistency, safety, and latency.
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Eval framework awareness : a working understanding of LLM evaluation concepts: scoring metrics (BLEU, ROUGE), LLM-as-judge patterns, and familiarity with at least one eval framework (DeepEval, RAGAS, etc.).
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CI/CD and continuous testing: experience integrating test suites into pipelines (GitHub Actions, CircleCI, or equivalent) with a shift-left mindset that treats test failures as blocking signals, not background noise.
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Quality ownership mentality: demonstrated ability to own quality outcomes, not just execute tasks; comfort setting standards, raising risk flags, and influencing cross-functional teams.
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Solid understanding of architectural patterns, microservices, and API testing (REST, gRPC) using tools like Postman or custom frameworks.
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Experience with containerization technologies (Docker) and orchestration (Kubernetes) as they relate to scalable testing environments.
Nice-to-Haves
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Experience with agentic systems testing: validating goal-completion rates, guardrail enforcement, and multi-step reasoning chains in LLM agent workflows.
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Familiarity with AIOps/MLOps/LLMOps concepts: prompt versioning, model monitoring, canary deployments, and drift detection.
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Experience with accessibility or security testing as part of a broader non-functional quality practice.
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Background in red teaming, adversarial input testing, or prompt injection validation.
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Experience working in a startup or scale-up environment where processes are built from scratch rather than inherited.
Why Togal?
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Join a dynamic team of AI-native engineering team.
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AI-native from day one - you'll be building the quality discipline for a product that uses AI at its core, making every quality decision novel and impactful.
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Be the quality voice, not a quality follower - this role has direct influence over how Togal defines and measures product excellence.
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A culture of innovation, continuous learning, and high growth.
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Comprehensive benefits
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Competitive compensation package and flexible work arrangements.
We are an equal opportunity employer committed to building a diverse team. We welcome applications from candidates of all backgrounds who are passionate about using technology to transform the construction industry.
Join us in revolutionizing pre-construction estimating with the power of AI!