Asks for

The ad says“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.”
The ad says“AI fluency”
The ad says“Deep, hands-on proficiency with AI-powered development tools (e.g., GitHub Copilot, Cursor, Claude Code) — you don't just use them, you've developed workflows and best practices around them that you can teach others.”
The ad says“Solid understanding of AI/ML fundamentals: transformer architectures, embedding models, inference optimization, RAG patterns, fine-tuning vs. prompt engineering trade-offs, and evaluation methodologies.”
The ad says“Strong understanding of caching strategies (Redis/Memcached), asynchronous messaging (Kafka/RabbitMQ), and event-driven architectures.”
The ad says“Expert in at least one modern backend programming language (e.g., Python, Ruby, Go, Java, or Node.js) with strong fundamentals across the stack.”
The ad says“Strong understanding of caching strategies (Redis/Memcached), asynchronous messaging (Kafka/RabbitMQ), and event-driven architectures.”
The ad says“Hands-on experience with containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code practices.”
The ad says“Deep, hands-on proficiency with AI-powered development tools (e.g., GitHub Copilot, Cursor, Claude Code) — you don't just use them, you've developed workflows and best practices around them that you can teach others.”
The ad says“Hands-on experience with containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code practices.”
The ad says“Deep, hands-on proficiency with AI-powered development tools (e.g., GitHub Copilot, Cursor, Claude Code) — you don't just use them, you've developed workflows and best practices around them that you can teach others.”
The ad says“Proven ability to design and build distributed systems — you've made meaningful architectural decisions around service decomposition, data consistency, fault tolerance, and observability.”
The ad says“Hands-on experience with containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code practices.”
The ad says“Solid understanding of AI/ML fundamentals: transformer architectures, embedding models, inference optimization, RAG patterns, fine-tuning vs. prompt engineering trade-offs, and evaluation methodologies.”
The ad says“Deep, hands-on proficiency with AI-powered development tools (e.g., GitHub Copilot, Cursor, Claude Code) — you don't just use them, you've developed workflows and best practices around them that you can teach others.”
The ad says“Solid understanding of AI/ML fundamentals: transformer architectures, embedding models, inference optimization, RAG patterns, fine-tuning vs. prompt engineering trade-offs, and evaluation methodologies.”
The ad says“Hands-on experience with containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code practices.”
The ad says“Strong understanding of caching strategies (Redis/Memcached), asynchronous messaging (Kafka/RabbitMQ), and event-driven architectures.”
The ad says“Hands-on experience with containerization (Docker/Kubernetes), CI/CD pipelines, and infrastructure-as-code practices.”
The ad says“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.”
The ad says“Strong understanding of caching strategies (Redis/Memcached), asynchronous messaging (Kafka/RabbitMQ), and event-driven architectures.”
The ad says“Deep expertise with relational databases (PostgreSQL, MySQL) and NoSQL stores, including schema design, query optimization, and data modeling for high-throughput workloads.”
The ad says“Deep expertise with relational databases (PostgreSQL, MySQL) and NoSQL stores, including schema design, query optimization, and data modeling for high-throughput workloads.”
The ad says“Strong understanding of caching strategies (Redis/Memcached), asynchronous messaging (Kafka/RabbitMQ), and event-driven architectures.”
The ad says“Solid understanding of AI/ML fundamentals: transformer architectures, embedding models, inference optimization, RAG patterns, fine-tuning vs. prompt engineering trade-offs, and evaluation methodologies.”
The ad says“Strong understanding of caching strategies (Redis/Memcached), asynchronous messaging (Kafka/RabbitMQ), and event-driven architectures.”
The ad says“Deep expertise with relational databases (PostgreSQL, MySQL) and NoSQL stores, including schema design, query optimization, and data modeling for high-throughput workloads.”
The ad says“Solid understanding of AI/ML fundamentals: transformer architectures, embedding models, inference optimization, RAG patterns, fine-tuning vs. prompt engineering trade-offs, and evaluation methodologies.”

HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world’s most innovative companies to build strong engineering teams ready for what’s next.

Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills.

People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards.

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 don't just use them, 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.
You actively follow developments in AI research and tooling, can 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.

You will thrive in this role if

You think beyond the immediate task and consider the long-term health, extensibility, and operational cost of the systems you build.
You take ownership not just of your own code, but of the overall quality and direction of the systems your team delivers.
You are energized by ambiguity — you can take a loosely defined problem, structure it, and drive it to a well-engineered solution.
You lead by influence, earning trust through strong technical judgment and a collaborative, ego-free approach.
You genuinely enjoy making other engineers better through mentorship, knowledge sharing, and raising the engineering bar.

Compensation

The base salary range for this role is $150,000 – $172,000, plus a target 10% annual bonus tied to individual and company performance. You will also receive equity (stock options) and a comprehensive package of cash and non-cash benefits.

Want to learn more about HackerRank? Check out HackerRank.com to explore our products, solutions and resources, and dive into our story and mission here.

HackerRank is a proud equal employment opportunity and affirmative action employer. We provide equal opportunity to everyone for employment based on individual performance and qualification. We never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.

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