Performance Engineering Lead

At Appspace, we’re passionate about creating better work experiences for people everywhere, and we’re looking for people that feel the same way. Our global office locations and flexible work culture help you work wherever and however you’re at your best. Plus, we take the time to help you enjoy your work, build lasting connections, and grow your role. Join the Appspace team and be a part of a culture that’s helping people everywhere love where they work.

As the Performance Engineering Lead, you will be responsible for building and owning Appspace's performance engineering capability from the ground up. This is a foundational role — you will define the strategy, select the toolchain, establish the first baselines across our product suite, and integrate performance gates directly into our CI/CD pipeline. You will serve as the technical authority on all performance decisions, ensuring that every service boundary in Appspace's platform is covered by a measurable, enforceable performance contract. As we scale our AI-driven development lifecycle (AIDLC), you will also ensure that AI-generated code meets the same performance standards as human-written code — making automation the enforcer of quality, not a bypass of it.

• Own end-to-end performance testing strategy across all Appspace product lines — Digital Signage, Space Reservation, Visitor Management, and Intranet.
• Design, build, and maintain a performance testing framework using k6 (primary) and Locust, with a baseline artifact store in Google Cloud Storage.
• Establish performance baselines for critical user journeys and service boundaries, starting with Signage as the highest-traffic product line.
• Integrate automated performance gates into the CI/CD pipeline, ensuring regressions are caught before they reach production.
• Leverage AI and AI tools throughout the entire test lifecycle—from generating test cases, optimizing test execution schedules, to autonomously monitoring test results and identifying performance degradation patterns.
• Define and enforce performance SLAs — including p95 latency targets, throughput thresholds, and error rate limits — across all product domains.
• Work closely with AI Delivery Leads in each product squad to ensure that AI-generated code touching service boundaries triggers automated load test candidates in the pipeline.
• Co-author performance test plans with domain engineers across Signage, Space+Visitor, Intranet, and Core Platform squads.
• Build and operate regression detection alerting, feeding results into the AIDLC metrics framework and engineering leadership dashboards.
• Champion performance engineering best practices across globally distributed engineering teams.
• Communicate performance posture, test coverage progress, and regression trends accurately and regularly to engineering leadership.
• Grow the function over time — mentoring a Phase 2 AI QE / Performance Engineer sourced from the internal QA retraining programme.

• Degree in Computer Science, Software Engineering, or equivalent.
• Minimum 7 years' experience in software engineering including at least 4 years of strong focus on performance testing, load testing, or site reliability engineering for multi-tenant, SaaS applications.
• Proven hands-on experience designing and executing load and performance test strategies at scale — not just running tools, but owning the programme.
• Strong proficiency with k6 and/or Locust for scripting and executing load tests against REST APIs and web applications.
• Experience integrating performance tests into CI/CD pipelines (e.g., GitHub Actions, Bamboo, Jenkins) as quality gates.
• Experience with Google Cloud Platform (GCP) and Kubernetes — ability to reason about infrastructure behaviour under load.
• Practical familiarity and hands-on experience using Generative AI tools (e.g., Claude, Cursor) to augment test development, scripting, or analysis workflows.
• Proficiency in Python and/or JavaScript/TypeScript for test scripting and tooling automation.
• Strong understanding of microservices architecture, API design, and distributed system performance characteristics.
• Experience with observability tooling — Grafana, Prometheus, Cloud Monitoring, or equivalent — for baseline measurement and regression detection.
• Experience with version control tools (Git) and familiarity with trunk-based development workflows.
• Strong analytical and problem-solving skills — able to diagnose performance regressions from first principles, not just tooling output.
• Excellent communication skills, both written and verbal — able to translate technical performance data into clear engineering and leadership narratives.
• Ability to work independently in a globally distributed team environment across time zones.

• Experience working with or evaluating AI-generated code in an engineering pipeline context.
• Familiarity with chaos engineering principles and tooling (e.g., Chaos Monkey, Gremlin).
• Experience with SQL and/or NoSQL databases (e.g., MongoDB, PostgreSQL) and understanding of database performance under load.
• Experience with BrowserStack or equivalent cross-browser and cross-device performance testing.
• Background in SRE or platform engineering at a SaaS company.

Appspace is committed to equitable compensation practices and complies with all applicable local, state, and federal regulations. For jurisdictions that require pay scale disclosure, a general compensation range may be provided during the initial stages of the interview process. Final compensation will be based on multiple factors including experience, skills, certifications, and overall fit for the role.

If you are located in a jurisdiction with specific pay transparency requirements, we will be happy to discuss the relevant range during your application process.