Staff Engineer Platform Engineering
HighLevel
Description
About HighLevel:
HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes.To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently.Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.
Our PeopleWith over 2,000 team members across 10+ countries, HighLevel operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.
Our ImpactEvery month, HighLevel enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. Weâre proud to be a part of that.Learn more about us on our YouTube Channel or Blog Posts
The Staff Engineer is expected to be a deeply technical engineering leader who thrives in ambiguity, takes extreme ownership, and has proven experience designing and building highly scalable, complex distributed systems at scale. You will also help drive engineering excellence through effective adoption of AI-powered development workflows and systems that improve software development lifecycle (SDLC) efficiency, developer productivity, quality, and delivery velocity.
- Architect, develop, and maintain reusable frameworks, SDKs, and core platform services using Node.js and GoLang
- Build scalable, cloud-native solutions leveraging Google Cloud Platform (GCP)
- Design and optimize systems that efficiently handle large-scale data and high-throughput workloads
- Contribute to high-performance service architectures capable of handling massive scale with strong reliability and observability standards
- Lead technical design reviews, establish engineering best practices, and mentor senior engineers across teams
- Drive architectural decisions for scalability, performance, security, and maintainability
- Partner cross-functionally with product, infrastructure, and engineering teams to deliver reliable platform capabilities
- Improve developer productivity by building internal tooling, shared libraries, and scalable engineering foundations
- Lead initiatives that leverage AI effectively across engineering workflows, including code generation, testing, developer tooling, incident analysis, and SDLC automation
- Build and evolve systems, platforms, and engineering processes that improve SDLC efficiency, release velocity, reliability, and developer experience
- Take ownership of critical platform initiatives from architecture through production operations
- 9+ years of software engineering experience with demonstrated success building distributed systems or large-scale backend services
- Hands-on experience designing, optimizing, and scaling large-scale backend systems and data-intensive applications
- Strong understanding of distributed systems, microservices, APIs, CI/CD pipelines, and observability frameworks
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About HighLevel
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Interview Prep Guide
Preparation Strategy
To prepare for this role, focus on reviewing system design principles, practicing technical discussions, and preparing examples of your experience with scalable architectures and cloud-native solutions. Be ready to discuss trade-offs and scalability considerations, and practice explaining technical concepts to non-technical stakeholders. Additionally, review your experience with Node.js and GoLang, and be prepared to discuss how you've applied them in previous projects.
Likely Interview Rounds
- 1. Technical~60 min
What to prep: Review system design principles, practice explaining technical concepts, and prepare examples of scalable architectures you've designed or worked with.
- How would you design a scalable architecture for a cloud-native application?
- What are some strategies for optimizing system performance under high-throughput workloads?
- Can you explain the trade-offs between monolithic and microservices architecture?
- How do you approach technical design reviews and establish engineering best practices?
- 2. System design~90 min
What to prep: Review system design patterns, practice designing systems on a whiteboard, and prepare to discuss trade-offs and scalability considerations.
- Design a system to handle large-scale data processing and analytics.
- How would you architect a real-time messaging system with high availability and low latency?
- Can you design a scalable and secure authentication system for a cloud-based platform?
- 3. Behavioral~60 min
What to prep: Prepare examples of your experience leading technical discussions, driving engineering excellence, and collaborating with cross-functional teams.
- Can you describe a time when you had to lead a technical design review or establish engineering best practices?
- How do you approach mentoring senior engineers or driving technical decisions across teams?
- Tell me about a project where you had to drive architectural decisions for scalability, performance, or security.
Most Likely Questions
- What are your thoughts on the trade-offs between scalability, performance, and maintainability in system design?
- Can you explain your experience with cloud-native solutions and Google Cloud Platform (GCP)?
- How do you approach technical debt and prioritize refactoring or optimization efforts?
- Can you describe your experience with Node.js and GoLang, and how you've applied them in previous projects?
Common Pitfalls
- Lack of experience with scalable system design and cloud-native solutions
- Insufficient understanding of trade-offs between different architectural approaches
- Inability to communicate technical concepts effectively to non-technical stakeholders
Free Prep Resources
- • System Design Primer (GitHub: donnemartin)
- • Designing Data-Intensive Applications by Martin Kleppmann
- • Cloud Native Patterns by Bilgin Ibryam