Job Description
Job Description
We are looking for a Software Engineer II to join one of our Scrum teams building AI-native, event-driven services for automotive retail intelligence. This is a core contributor role for engineers with 2–4 years of experience who are ready to take end-to-end ownership of features, grow their distributed systems skills, and work in a high velocity environment that demands code quality, hexagonal architecture discipline, and genuine curiosity about AI-native engineering.
Key Responsibilities
Develop and maintain Java (Spring Boot 3.x) backend services — implementing domain logic, REST APIs, Kafka event consumers/producers, and MongoDB/PostgreSQL persistence adapters following hexagonal architecture patterns.
Build features across the full backend slice: domain model, port interface, adapter implementation, unit tests, and integration tests.
Write integration tests using Testcontainers for MongoDB, PostgreSQL, Kafka, and Elasticsearch adapters — ensuring each adapter is tested independently from the domain core.
Participate in design discussions, architecture reviews, and code reviews — actively learning from Staff and Senior engineers.
Investigate and resolve bugs, performance issues, and Kafka consumer lag across services.
Write clean, well-documented, testable code with consistent application of hexagonal design principles.
Contribute to golden dataset test scenario design under the guidance of the Product Manager and SDET.
Operate with ownership and curiosity in a fast-paced, startup environment.
Skills and Experience
2–4 years of backend engineering experience; Bachelor's or Master's degree in Computer Science or a related field.
Solid Java fundamentals: object-oriented design, Spring Boot, REST API implementation, and unit testing (JUnit 5).
Working knowledge of at least one of: MongoDB, PostgreSQL, or Kafka — with willingness to develop depth across all three.
Understanding of hexagonal architecture concepts (ports & adapters) — ability to implement adapters under the guidance of senior engineers.
Familiarity with cloud environments (AWS), containerization (Docker), and CI/CD pipelines.
Strong problem-solving skills, attention to code quality, and ability to receive and apply feedback quickly.
Collaborative mindset and clear communication — comfortable asking questions and flagging blockers early.
Preferred Skills
Exposure to Kafka producers and consumers in a production or learning context.
Familiarity with Elasticsearch for search and analytics use cases.
Curiosity about AI/ML systems — awareness of what scoring models, embeddings, and LLMs do at a systems level.
Exposure to Redis or Aerospike for caching patterns.
Perks and Benefits
Competitive compensation and generous stock options.
Medical insurance coverage.
Work with some of the brightest minds from Silicon Valley's most dominant and successful companies.