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As passionate about our people as we are about our mission.

Why Join Q2?

Q2 is a leading provider of digital banking and lending solutions to banks, credit unions, alternative finance companies, and fintechs in the U.S. and internationally. Our mission is simple: build strong and diverse communities through innovative financial technology—and we do that by empowering our people to help create success for our customers.

What Makes Q2 Special?

Being as passionate about our people as we are about our mission. We celebrate our employees in many ways through our year-round Q2 ChangeMakers awards program and global moments of recognition and connection. We invest in the growth and development of our team members through ongoing learning opportunities, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun and giving back together. From company-wide volunteer days to events like our Q2 Homecoming Week—featuring learning, community service, and culture-building experiences—we create opportunities to connect, grow, and make an impact.

The Job At-A-Glance

Join a fast-growing AI engineering team building the next generation of intelligent products for the financial services industry. As a Machine Learning Engineer, you'll design and deliver production-ready AI solutions, develop the tools, APIs, and backend services that enable them, and help shape the future of our AI platform. From evaluation frameworks, deployment pipelines, and microservice-based AI capabilities to next-generation agentic workflows, you'll have the opportunity to work across the stack, prototype new ideas, and turn innovation into real customer value. We're looking for engineers who are passionate about technology, committed to building high-quality, enterprise-grade software, and who thrive in collaborative environments. The ideal candidate stays current with the rapidly evolving AI landscape, enjoys solving meaningful problems, and is excited to help build innovative solutions alongside a team that values creativity, ownership, continuous learning, and shipping great products.

A Typical Day

• Build, test, and optimize production-ready LLM agents leveraging multi-step reasoning, prompt engineering, retrieval, memory, and tool-calling capabilities.
• Develop scalable evaluation pipelines (Evals) to continuously measure accuracy, task completion, latency, cost efficiency, safety, and hallucination rates.
• Integrate agentic workflows with external platforms, vector databases, microservices, MCP, and internal APIs to enable reliable context retrieval and automated action-taking.
• Fine-tune domain-specific LLMs using modern adaptation techniques like supervised fine-tuning (SFT), LoRA/PEFT, and preference optimization to maximize performance.