Software Analysis Engineering

Software Engineer I

As part of Cox Automotive's Consumer & Marketing Solutions Portfolio, our Demand Side Platform team builds real-time bidding and programmatic advertising infrastructure that powers vehicle marketing at scale. We operate within a SAFe Agile Release Train, delivering value through well-defined Value Streams that connect product capabilities to measurable business outcomes.

We are looking for a Software Engineer I who is eager to learn, grow, and contribute to a high-impact team from day one. This is an early-career role designed for engineers who bring foundational technical skills, intellectual curiosity, and a genuine enthusiasm for modern software development practices — including the responsible use of AI-assisted development tools.

In this role, you will work alongside experienced engineers to understand business requirements, develop your technical craft, and contribute to building scalable, resilient, and well-tested systems. You will begin learning how to leverage AI tools such as GitHub Copilot, Claude Code, and MCP-based workflows to support coding, testing, and documentation tasks — always under the guidance of senior team members and with a focus on validating and taking ownership of all AI-generated output.

Cox Automotive's Advertising & Marketing teams are spread across Atlanta, GA, Austin, TX, and Burlington, VT. Our Demand Side Platform team operates at the intersection of high-throughput data systems, real-time decisioning, and cloud infrastructure. We are a collaborative team that values engineering craft, continuous improvement, and building systems that scale. If you are excited to grow your skills in a real-world, production engineering environment where AI tooling is a first-class part of the workflow — you'll fit right in.

Contribute to coding tasks by writing unit and system tests, participating in pair programming, and using AI tools to support code generation, documentation, and troubleshooting — always reviewing output for accuracy, security, and alignment with engineering standards.

Work within your team to understand and break down epics, features, and stories, building familiarity with how requirements are shaped using AI for problem and solution discovery.

Learn and apply spec-driven development (SDD) practices by participating in specification reviews and contributing to well-structured specs before implementation begins.

Develop foundational skills across the team's core technology stack — including Java, TypeScript, and Python — with a focus on writing clean, readable, and maintainable code.

Build familiarity with AWS cloud services and begin learning infrastructure-as-code concepts (Terraform, AWS CDK) under the guidance of senior engineers.

Gain exposure to relational and non-relational data stores (SQL/NoSQL) and in-memory caching concepts (Redis/Valkey) as part of contributing to data-driven services.

Learn to troubleshoot issues using traditional debugging techniques and, progressively, AI-assisted root-cause exploration to diagnose and resolve problems.

Support platform modernization efforts by assisting with legacy code updates and migration tasks, using AI tools to understand older patterns and propose alternatives.

Learn and apply secure coding practices and participate in AI-assisted code analysis to identify vulnerabilities or quality concerns.

Document processes, patterns, and best practices, leveraging AI tools appropriately to produce clear and consistent documentation.

Share observations and lessons learned from AI-assisted workflows to help refine team practices.

Bachelor's degree in Computer Science, Software Engineering, or a related discipline. The right candidate could also have a different combination, such as 4 years' experience in a related field.

Foundational programming skills in one or more modern languages; exposure to Java, TypeScript, or Python is a plus.

Basic understanding of software development concepts including version control, testing, debugging, and the software development lifecycle.

Eagerness to learn and grow across a broad technology stack including cloud platforms, data stores, and infrastructure tooling.

Openness to learning and responsibly adopting AI-assisted development tools (such as GitHub Copilot, Claude Code, or MCP-based workflows) as part of everyday engineering practice.

Familiarity with or willingness to learn Agile and/or Value Stream-based delivery models.

Academic or internship exposure to cloud platforms, particularly AWS.

Coursework or project experience with relational/non-relational databases, caching, or distributed systems concepts.

Basic understanding of CI/CD pipelines, containerization, or infrastructure-as-code concepts.

Demonstrated ability to learn new technologies quickly through personal projects, open-source contributions, or academic work.