AI-Driven Java Engineer for High-Throughput Access Checker

OCLC Online Computer Library Center, Inc in; Dublin (OH); Hybrid; USD 90,000 - 130,000; Full time

Benefits offered by this job

Hybrid work environment

Onsite fitness center

Tuition reimbursement

Parental leave

Cafeteria subsidies

Health and retirement benefits

Job summary

OCLC Online Computer Library Center, Inc. seeks a Software Engineer to contribute to the Access Checker system, building high-throughput link-harvesting and evaluation services.

The role emphasizes Java/Spring development, reliability, and collaboration within a dispersed team using CI/CD and AI-assisted tooling. You will work on integrating with other OCLC systems, addressing performance, observability, and production readiness while exploring ML/LLMs to enhance evaluation accuracy and

Qualifications

• Bachelor's degree or equivalent work experience.
• 2–4 years of production-quality software experience.
• Strong Java/Spring, OOP, testing, and performance focus.
• Experience with RESTful services and relational databases.

Responsibilities

• Design, implement, test, and support Access Checker components.
• Develop features for a high-throughput link-harvesting and evaluation pipeline.
• Write clean Java/Spring code and contribute to a reliable production service.
• Investigate performance, reliability, and integration concerns.

Skills

Java

Spring Framework

REST APIs

Distributed systems concepts

Problem solving

AI tools familiarity

Team collaboration

Education

Bachelor's degree in CS or related

Tools

React

Python

CI/CD

SQL

Job description

OCLC Online Computer Library Center, Inc. seeks a Software Engineer to contribute to the Access Checker system, building high-throughput link-harvesting and evaluation services.

The role emphasizes Java/Spring development, reliability, and collaboration within a dispersed team using CI/CD and AI-assisted tooling. You will work on integrating with other OCLC systems, addressing performance, observability, and production readiness while exploring ML/LLMs to enhance evaluation accuracy and