Top AI & Machine Learning Jobs

Conduct applied AI/ML research focused on software engineering, code quality, testing, and maintenance. Design experimental prototypes, develop Python-based AI/ML solutions, collaborate with researchers, engineers, and product managers, publish findings, participate in code reviews, and apply DevOps practices to maintain reliable research infrastructure.

Own and evolve Sonar’s AI-native internal technology ecosystem. Build AI agents, MCP integrations, enterprise connectors, workflow automations, and integrations across Atlassian and other enterprise systems. Administer Jira Service Management and the Atlassian Cloud suite, manage CMDB and assets, enforce ITSM and security governance, support audits, and partner with internal teams to deliver scalable platform improvements.

Own and evolve Sonar’s AI-native internal technology ecosystem. Build Claude MCP integrations, Rovo agents, connectors, workflow automations, and enterprise integrations while administering Jira Service Management and the Atlassian Cloud suite. Manage CMDB and asset data, ITSM governance, security controls, audit support, and platform roadmap initiatives. Partner with internal teams to automate processes, improve operational scalability, and deliver governed, reliable AI-augmented systems.

The role involves developing AI models for code analysis, enhancing ML techniques specific to programming, and collaborating to integrate prototypes into products.

As a Machine Learning Scientist, you'll innovate in code analysis using AI and LLMs, develop advanced ML models, and collaborate to enhance software quality assurance.

Build AI-native program-analysis systems that combine static/dynamic analysis and LLMs to detect and fix bugs, validate fixes against CI, index and retrieve code context, orchestrate review agents, and own production qualities like correctness, latency, cost, and security while working with customers and SonarQube integrations.

Own product-usage analytics from exploration and experimentation through actionable insights. Analyze adoption, engagement, retention, expansion, churn risk, and account health by connecting product, customer, and sales data. Build usage-based signals, validate predictive metrics statistically, partner with data engineers on warehouse models, and communicate recommendations that influence product and go-to-market decisions.

Design, build, and maintain AI agents and automated marketing workflows. Map automation opportunities, deploy and monitor agents, integrate with marketing tools, produce impact reporting, ensure data governance, and document/enable marketing teams.

Build and maintain AI agents and automated workflows for marketing: identify automation opportunities, design and deploy agents, monitor performance, ensure data governance, integrate with marketing tools, and report efficiency and business impact to leadership.

Design, build, and maintain AI agents and automated workflows for marketing. Identify automation opportunities, deploy and monitor agents, integrate with marketing tools, and report efficiency and business impact. Ensure data quality, privacy, and documentation, and enable marketing teams to adopt automations.