Top AI & Machine Learning Jobs

Lead GitLab’s company-wide strategy for autonomous, agentic software development workflows. Identify and validate AI opportunities across the SDLC, build prototypes and reference architectures, establish evaluation frameworks and safety controls, and drive internal adoption. Partner across Engineering, Product, Infrastructure, Security, Architecture, and Data and ML teams to scale reliable, observable, compliant agentic capabilities into customer-facing products. Mentor senior technical leaders and represent GitLab externally on AI-assisted development and productivity measurement.

Build and deploy autonomous AI agents that automate software development lifecycle tasks, including merge request authoring, test generation, and security remediation. Develop orchestration patterns, safety guardrails, and reliable distributed services; evaluate performance, latency, and correctness; participate in on-call support; collaborate with Product and AI/ML teams; review code, document practices, and mentor engineers.

Own the technical direction for validating AI agent-generated software. Build prototypes and platform systems that identify gaps between passing tests and working features, provide mid-loop feedback for agent self-correction, and support trusted development across Rust, Ruby, and Go. Collaborate with engineering teams to drive adoption, measure trust and validation quality, establish architecture, review designs, and mentor teammates in a remote asynchronous environment.

Lead and grow GitLab’s Trusted Agentic Development engineering team. Define approaches for validating AI-generated software, improve agent feedback loops, support language-aware validation across Rust, Ruby, and Go, and drive adoption through useful platform capabilities. Partner with engineering teams, measure trust and usage, guide architecture and design, hire and develop engineers, manage performance, and lead complex work from planning through completion in a remote, asynchronous environment.

As Principal Product Manager, you'll define product strategy for GitLab's AI models, focusing on custom and self-hosted AI capabilities, collaborating with engineering, and ensuring alignment with company strategy.

Build and maintain the Python-based backend engine for GitLab Duo Chat, including agentic LangGraph flows, the Flow Registry, and Duo Workflow Service. Integrate chat capabilities with GitLab’s Rails monolith and GraphQL API, develop secure and tested high-scale features, optimize performance, review code, address technical debt, and collaborate cross-functionally. Participate in weekday, weekend, and occasional nighttime Tier 2 or Tier 3 on-call rotations.

Build and deploy internal AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify bottlenecks, determine whether AI is appropriate, and own initiatives from discovery through deployment. Develop integrations, prototypes, agentic architectures, prompts, guardrails, and evaluation frameworks while measuring business, flow, adoption, and ROI outcomes. Partner with stakeholders, assess AI tools and models, document reusable patterns, and advance GitLab’s AI-first transformation in a remote environment.

Build and deploy AI-powered solutions across Sales, Marketing, and Customer Support. Diagnose workflows, identify constraints, evaluate whether AI is appropriate, and own initiatives from discovery through deployment. Develop LLM applications using prompt engineering, RAG, agentic architectures, orchestration, guardrails, and enterprise integrations. Measure business value through flow metrics, adoption, ROI, and feedback loops while partnering with technical and non-technical stakeholders.

The role involves being a technical advisor, focusing on AI solutions within GitLab's DevSecOps platform, guiding strategy and value for customers, and leading technical evaluations and workshops.

Build and maintain backend AI capabilities for GitLab Duo Chat: design flow components and agentic flows in Python/LangGraph, integrate LLMs and multi-agent orchestration, implement and maintain APIs in the Rails monolith, improve observability and reliability, and participate in on-call rotations and cross-functional collaboration to deliver safe, scalable AI chat features.