This is a remote position.
We're hiring a Backend Engineer. You'll build and maintain scalable backend services that power experiences for hundreds of millions of job seekers and employers globally.
This is a hands-on engineering role with general independence — you'll own meaningful pieces of work, make day-to-day technical decisions, and collaborate with product, design, and platform teams to ship features that move the needle.
Design, build, and maintain backend services using Java/Kotlin, Spring Boot, and Spring Batch, with a focus on reliability and performance at scale.
Develop and evolve GraphQL and REST APIs that serve internal services and consumer-facing products.
Build and tune batch processing pipelines for data-intensive workflows (ingestion, enrichment, scheduled jobs).
Leverage LLM-assisted coding tools (e.g., Claude Code, Copilot) to accelerate development, and apply sound judgment to review, validate, and refine AI-generated code.
Analyze tradeoffs across performance, maintainability, and delivery speed; apply judgment within established engineering procedures and patterns.
Participate in code reviews, on-call rotations, and incident response — contributing to service health, observability, and continuous improvement.
Partner with product managers, architects, and fellow engineers to scope work, refine requirements, and deliver against team objectives.
Must-Have Skills (Required):
3+ years of professional backend engineering experience in Java (primary) or Kotlin.
Experience working in Java, while also being comfortable working in Kotlin environments or codebases.
Production experience with Spring Boot and Spring Batch (or equivalent JVM batch frameworks).
Strong fundamentals in REST API design and implementation. Hands-on experience building or consuming GraphQL APIs (federated graphs a plus).
Strong working with LLM-based coding assistants as part of your day-to-day workflow — including knowing when to trust output and when to push back. (Cursor, GitHub Copilot, Claude, or similar.)
Solid understanding of relational and/or NoSQL data stores, async messaging (Kafka or similar), and distributed systems concepts.
Ability to operate with general independence on moderately complex problems and deliver quality work without close supervision.
Note: Candidate should have demonstrated ability to speak credibly and practically about the use of LLM-based coding assistants within real-world engineering workflows — including how AI tools are leveraged to improve productivity, accelerate development, validate outputs, and apply engineering judgment rather than simply stating general AI tool usage experience.
Nice to Have:
Experience operating services at large scale (millions+ requests/day).
Exposure to GraphQL federation (Apollo Federation or similar).
Observability tooling experience (Datadog, OpenTelemetry, Prometheus).
Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms.

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