Top Tech Jobs & Startup Jobs in Los Angeles, CA

As a Software Engineering Manager, lead multiple software engineering teams, guide best practices, drive innovation, and support career development while collaborating with product management.

Develop and implement AI infrastructure, focusing on distributed systems and cloud-native architecture to enable agentic workflows and high-scale data operations.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Design, develop, and maintain scalable, high-performance web applications and shared component libraries. Implement automated testing and CI/CD, improve scalability and reliability, troubleshoot production incidents, provide live-site support, own cross-team projects, and collaborate with Product and Design to deliver accessible, performant user experiences for fleet-management platforms.

Lead and execute go-to-market strategy for fuel, EV, and telematics solutions, while building a high-performing product marketing team focused on customer insight and sales enablement.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.

Lead Data Acquisition Team, drive AI-augmented development, establish specification-driven methodology, oversee data pipeline architecture and ensure operational excellence.

Process and post customer payments (ACH, wires, lockbox), reconcile payment discrepancies, handle adjustments/write-offs/refunds, research accounting issues, support and document AR processes and controls, train other AR specialists, and monitor inboxes to resolve customer posting inquiries promptly.

Build and deploy credit and fraud risk models using advanced ML/statistical methods. Partner with stakeholders, extract insights from large transaction datasets, automate feature/production pipelines, mentor junior data scientists, and ensure model quality and deployment scalability.

The role involves leveraging advanced analytics and machine learning to assess and mitigate credit and fraud risks, developing scalable modeling solutions while collaborating with stakeholders.