Job Description

About the Role

We're looking for a

Senior Software Engineer (L5a)

to join the

Security Risk & Compliance Automation

team. In this role, you'll design and scale backend systems that power Uber's global Governance, Risk, and Compliance (GRC) infrastructure. You will take ownership of complex problems that span multiple areas, architect solutions for reliability and performance, and influence technical direction within your team.



This is a hands-on role for engineers who thrive on solving ambiguity, writing high-quality code, and raising the bar for engineering excellence.

What the Candidate Will Need / Bonus Points

- What the Candidate Will Do -

Lead the design and development of backend services that automate risk and compliance processes at global scale. Drive technical decisions for projects with multiple dependencies, making thoughtful tradeoffs between short- and long-term priorities. Act as an escalation point for production incidents, applying structured debugging and ensuring follow-through on root cause fixes. Mentor junior engineers, elevate team coding standards, and contribute to reusable frameworks and patterns. Partner cross-functionally with Security, Compliance, and Platform teams to align technical solutions with business objectives.


- Basic Qualifications -

5+ years

of software development experience in backend or distributed systems. Proficiency in one or more programming languages (e.g., Go, Java, Python, C++). Experience designing, implementing, and maintaining

APIs

and microservices. Strong understanding of databases (SQL/NoSQL), data modeling, and system performance optimization. Familiarity with CI/CD pipelines, cloud-native infrastructure, and containerization (Docker, Kubernetes). Demonstrated ability to navigate tradeoffs and deliver results in complex projects. Experience with distributed data processing frameworks (e.g., Kafka) for building robust data pipelines.


- Preferred Qualifications -

Bachelor's or Master's degree in Computer Science or related field. Prior experience building

large-scale backend platforms

or infrastructure. Familiarity with applications of Natural Language Processing (NLP) or Large Language Models (LLMs) Knowledge of data structures and algorithms applied in production. Experience with monitoring, observability, and operational excellence at scale. Proven experience building backend infrastructure or services that directly support AI/ML-powered applications (e.g., model serving APIs, feature stores, or data validation systems). * Knowledge of specific compliance or risk management domains (e.g.,ISO 2700, ISO 4200, etc.) and experience building technology to address their requirements.