Senior Backend Engineer

HackerRank Careers•Santa Clara, CA

6h•$190,000 - $220,000

About The Position

HackerRank is seeking a senior backend engineer to drive the technical direction of critical backend systems and lead high-impact, platform-level initiatives. The role owns the architecture and evolution of core backend services, helps ensure the platform remains fast, reliable, and scalable as its global user base grows, and provides technical leadership and mentorship across teams. The engineer will shape backend architecture and domain strategy, guide complex system design and implementation, and own reliability and performance practices for critical services. The role also involves cross-functional technical leadership, modernizing legacy systems, and improving engineering standards and developer productivity. HackerRank emphasizes high standards, urgency, customer impact, and collaborative, ego-free technical leadership.

Requirements

3–6 years of experience building and operating production backend systems at scale.
Expertise in at least one modern backend programming language, such as Python, Ruby, Go, Java, or Node.js, with strong fundamentals across the stack.
Proven ability to design and build distributed systems, including architectural decisions involving service decomposition, data consistency, fault tolerance, and observability.
Deep expertise with relational databases such as PostgreSQL or MySQL and NoSQL stores, including schema design, query optimization, and data modeling for high-throughput workloads.
Strong understanding of caching strategies such as Redis or Memcached, asynchronous messaging such as Kafka or RabbitMQ, and event-driven architectures.
Hands-on experience with containerization such as Docker or Kubernetes, CI/CD pipelines, and infrastructure-as-code practices.
Track record of leading technical projects from ambiguous problem statements through production delivery.
Deep, hands-on proficiency with AI-powered development tools such as GitHub Copilot, Cursor, or Claude Code, including developing workflows and best practices to teach others.
Strong working knowledge of LLMs and agentic AI systems, including model capabilities and limitations, context management, tool use, and backend integration considerations.
Ability to leverage AI across the software development lifecycle, including architecture exploration, implementation, code review, test generation, documentation, incident analysis, and technical writing.
Solid understanding of AI/ML fundamentals, including transformer architectures, embedding models, inference optimization, RAG patterns, fine-tuning versus prompt engineering trade-offs, and evaluation methodologies.
Ability to evaluate and recommend AI tooling, APIs, and integration patterns for a team and domain, accounting for cost, latency, reliability, and security.
Actively follow AI research and tooling, distinguish practical engineering value from hype, and drive adoption of AI-augmented practices within the team.

Nice To Haves

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 such as 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.
History of driving engineering culture improvements through practices such as RFC processes, architecture review boards, or engineering blog contributions.

Responsibilities

Architect, design, and lead implementation of complex backend systems and services that power core product experiences at scale.
Define and drive technical strategy for the domain, including system design, technology choices, and long-term architectural direction.
Own the reliability and performance of critical backend services, establishing SLOs, monitoring, and incident response best practices.
Design scalable API frameworks and data models that support multiple product teams and external integrations.
Lead cross-functional technical initiatives across teams, coordinating with frontend, infrastructure, product, and design stakeholders.
Identify and lead large-scale refactoring efforts to address technical debt and evolve legacy systems into maintainable architectures.
Mentor and develop engineers through design reviews, code reviews, and hands-on technical guidance.
Contribute to engineering-wide standards, tooling, and processes that improve code quality and developer productivity.
Develop and teach workflows and best practices for AI-powered development tools.
Evaluate AI tooling, APIs, and integration patterns, considering cost, latency, reliability, and security.