About HighLevel:
HighLevel is an AI powered, all-in-one white-label sales & marketing platform that empowers agencies, entrepreneurs, and businesses to elevate their digital presence and drive growth. We are proud to support a global and growing community of over 2 million businesses, comprised of agencies, consultants, and businesses of all sizes and industries. HighLevel empowers users with all the tools needed to capture, nurture, and close new leads into repeat customers. As of mid 2025, HighLevel processes over 4 billion API hits and handles more than 2.5 billion message events every day. Our platform manages over 470 terabytes of data distributed across five databases, operates with a network of over 250 microservices, and supports over 1 million hostnames.
Our People
With over 1,500 team members across 15+ countries, we operate in a global, remote-first environment. We are building more than software; we are building a global community rooted in creativity, collaboration, and impact. We take pride in cultivating a culture where innovation thrives, ideas are celebrated, and people come first, no matter where they call home.
Our Impact
As of mid 2025, our platform powers over 1.5 billion messages, helps generate over 200 million leads, and facilitates over 20 million conversations for the more than 2 million businesses we serve each month. Behind those numbers are real people growing their companies, connecting with customers, and making their mark - and we get to help make that happen.
About the Role:
We are looking for a Staff Software Development Engineer in Test with deep expertise in AI tools and model testing. This role requires an individual who can define overall QA strategy, architect automation frameworks, and drive organisation-wide quality initiatives for our AI products. You will own the quality charter for the AI vertical, influence cross-functional teams, and play a key role in shaping the AI quality roadmap.
Requirements:
- 8+ years of overall experience in QA/SDET roles
- Deep expertise in AI tools & model testing, responsible for designing scalable test approaches for RAG, hallucination, drift, and bias
- Proven experience in architecting large-scale, reusable, cross-platform automation frameworks
- Strong leadership skills to coach and upskill an entire team, set best practices, and build a talent pipeline
- Ability to influence cross-functional teams and participate in architecture discussions
- Strong problem-solving skills with the ability to own decisions on test strategy, tools, frameworks, and AI testing methodologies
Responsibilities:
- Define overall QA strategy and architect large-scale, reusable, cross-platform automation frameworks
- Drive organisation-wide quality initiatives and own the quality charter for the AI vertical
- Possess deep expertise in AI tools & model testing, responsible for designing scalable test approaches for RAG, hallucination, drift, and bias
- Own decisions on test strategy, tools, frameworks, and AI testing methodologies
- Coach and up-skill the entire team, set best practices, and build talent pipeline
- Influence cross-functional teams, participate in architecture discussions, and align testing with business strategy
- Responsible for end-to-end quality strategy at the product/vertical level
- Shape AI quality roadmap, reduce organisation-wide risk, and improve scalability and reliability
Bonus Points:
- Experience with CI/CD pipelines and cloud platforms (AWS/GCP/Azure)
- Familiarity with MLOps pipelines and monitoring AI systems
- Exposure to performance and scalability testing of AI workloads
EEO Statement:
The company is an Equal Opportunity Employer. As an employer subject to affirmative action regulations, we invite you to voluntarily provide the following demographic information. This information is used solely for compliance with government record-keeping, reporting, and other legal requirements. Providing this information is voluntary and refusal to do so will not affect your application status. This data will be kept separate from your application and will not be used in the hiring decision.
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