Senior Backend Engineer

About the Role

Every day, millions of developers use HackerRank to prove their skills. We're looking for a Senior Backend Software Development Engineer who can drive the technical direction of critical backend systems and lead the delivery of high-impact, platform-level initiatives. You will own the architecture and evolution of core backend services, mentor engineers across teams, and be a key technical decision-maker ensuring our platform remains fast, reliable, and scalable as we grow our global user base.

What You’ll Do

• Architect, design, and lead the implementation of complex backend systems and services that power core product experiences at scale.
• Define and drive technical strategy for your domain, making key decisions on system design, technology choices, and long-term architectural direction.
• Own the end-to-end reliability and performance of critical backend services, establishing SLOs, monitoring, and incident response best practices.
• Design scalable API frameworks and data models that serve as foundations for multiple product teams and external integrations.
• Lead cross-functional technical initiatives spanning multiple teams, coordinating with frontend, infrastructure, product, and design stakeholders.
• Identify and drive large-scale refactoring efforts, tackling tech debt and evolving legacy systems into modern, maintainable architectures.
• Mentor and grow engineers on the team through design reviews, code reviews, and hands-on technical guidance.
• Contribute to engineering-wide standards, tooling, and processes that raise the bar for code quality and developer productivity.

Who You Are

• Senior backend engineer with 3-6 years of experience building and operating production backend systems at scale.
• Expert in at least one modern backend programming language (e.g., Python, Ruby, Go, Java, or Node.js) with strong fundamentals across the stack.
• Proven ability to design and build distributed systems — you've made meaningful architectural decisions around service decomposition, data consistency, fault tolerance, and observability.
• Deep expertise with relational databases (PostgreSQL, MySQL) and NoSQL stores, including schema design, query optimization, and data modeling for high-throughput workloads.
• Strong understanding of caching strategies (Redis/Memcached), asynchronous messaging (Kafka/RabbitMQ), and event-driven architectures.
• Hands-on experience with containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code practices.
• Track record of leading technical projects from ambiguous problem statements through to production delivery.

AI Fluency

• Deep, hands-on proficiency with AI-powered development tools (e.g., GitHub Copilot, Cursor, Claude Code) — you've developed workflows and best practices around them that you can teach others.
• Strong working knowledge of LLMs and agentic AI systems — you understand model capabilities, limitations, context management, tool use, and can reason about when and how to integrate AI into backend systems.
• Proven ability to leverage AI across the full software development lifecycle: architecture exploration, implementation, code review, test generation, documentation, incident analysis, and technical writing.
• Solid understanding of AI/ML fundamentals: transformer architectures, embedding models, inference optimization, RAG patterns, fine-tuning vs. prompt engineering trade-offs, and evaluation methodologies.
• Ability to evaluate and make technical recommendations on AI tooling, APIs, and integration patterns for your team and domain — including cost, latency, reliability, and security considerations.
• Actively follow developments in AI research and tooling, separate hype from real engineering value, and drive adoption of AI-augmented practices within your team.

Even Better If You Have

• Experience designing and operating systems serving millions of concurrent users with strict latency and availability requirements.
• Deep expertise in system design patterns such as Microservices, CQRS, Event Sourcing, or Domain-Driven Design, with real-world application.
• Significant experience with cloud platforms (AWS, GCP, or Azure), including serverless architectures, managed services, and cost optimization.
• Experience building platform-level APIs, SDKs, or developer tools consumed by internal or external engineering teams.
• A history of driving engineering culture improvements — whether through RFC processes, architecture review boards, or engineering blog contributions.