Lead Software Engineer @ Here Technologies

• 8+ years of software development experience with a Bachelor s/Master s in Computer Science
• Strong problem-solving skills in system design and backend troubleshooting
• Expert in Java, Spring Boot, and microservices architecture
• Skilled in building scalable, low-latency cloud systems using AWS, Docker, and NGINX
• Solid experience with REST APIs, messaging frameworks, JSON/XML, and SOA
• Proficient in PostgreSQL and SQL with strong DBMS knowledge
• Writes clean, reusable, and testable code with a strong quality mindset
• Leads end-to-end software development lifecycle with minimal supervision
• Applies CI/CD and DevOps practices across the software stack
• Collaborates effectively with cross-functional teams and mentors junior engineers
• Agile, detail-oriented, self-motivated, and adaptable to fast-paced environment

HERE is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, age, gender identity, sexual orientation, marital status, parental status, religion, sex, national origin, disability, veteran status, and other legally protected characteristics.

HERE Technologies is a location data and technology platform company. We empower our customers to achieve better outcomes from helping a city manage its infrastructure or a business optimize its assets to guiding drivers to their destination safely.

At HERE we take it upon ourselves to be the change we wish to see. We create solutions that fuel innovation, provide opportunity and foster inclusion to improve people s lives. If you are inspired by an open world and driven to create positive change, join us. Learn more about us on our YouTube Channel.

You will be part of the Labeling as a Service (LaaS) team, which plays a critical role in supporting the Machine Learning (ML) team by ensuring high-quality dataset training through precise labeling, visualization, and feature extraction. The team is responsible for developing and maintaining annotation tools used by GCO users, as well as providing robust feature extraction capabilities to various stakeholders. Additionally, the LaaS team oversees essential infrastructure and processes that enable scalable and efficient data labeling operations.