Machine Learning Engineer

Being as passionate about our people as we are about our mission. We celebrate our employees in many ways, including our "Circle of Awesomeness" award ceremony and day of employee celebration among others! We invest in the growth and development of our team members through ongoing learning opportunities, mentorship programs, internal mobility, and meaningful leadership relationships. We also know that nothing builds trust and collaboration like having fun. We hold an annual Dodgeball for Charity event at our Q2 Stadium in Austin, inviting other local companies to play, and community organizations we support to raise money and awareness together., The Machine Learning Engineer provides hands-on expertise in designing, implementing, and scaling AI solutions, while collaborating with cross-functional teams to advance machine learning capabilities and support business innovation.

• Build and deploy AI agents powered by LLMs, including tool use, retrieval, memory, and orchestration workflows.
• Create robust eval frameworks to benchmark agent performance, safety, reliability, and cost efficiency.
• Integrate LLM agents with external tools, APIs, and MCP-based systems to enable action-taking and context-aware automation.
• Fine-tune and customize foundation models for domain-specific use cases using modern adaptation techniques.
• Monitor and improve production AI systems through experimentation, guardrails, and continuous performance optimization., * Design, build, and optimize AI agents using LLMs, prompt engineering, retrieval, memory, tool use, and multi-step reasoning to solve real-world workflows and business problems.
• Develop and maintain evaluation pipelines (Evals) to measure agent quality, including accuracy, task completion, hallucination rate, tool-calling correctness, latency, cost, and safety.
• Integrate agents with tools and systems through APIs, MCP, databases, vector stores, and internal platforms so agents can reliably access context, perform actions, and operate in production environments.
• Fine-tune and adapt LLMs using techniques such as supervised fine-tuning, instruction tuning, LoRA/PEFT, and preference optimization to improve domain performance, reliability, and response quality.
• Deploy, monitor, and improve agent systems in production by implementing guardrails, observability, experimentation, feedback loops, and continuous model/prompt updates based on user and eval data.

• Bachelor's degree in related field and 5-8 years relevant experience
• Proven experience in ML model development and deployment
• Strong knowledge of statistics, optimization, probability theory, and experimental methodologies
• Proficiency in programming languages such as Python, R, or Java
• Experience with ML frameworks/libraries (TensorFlow, PyTorch, scikit-learn)
• Familiarity with cloud platforms and scalable computing resources
• Strong analytical, problem-solving, and collaboration skills

This position requires fluent written and oral communication in English.

Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.

• Hybrid Work Opportunities
• Flexible Time Off
• Career Development & Mentoring Programs
• Health & Wellness Benefits, including competitive health insurance offerings and generous paid parental leave for eligible new parents
• Community Volunteering & Company Philanthropy Programs
• Employee Peer Recognition Programs - "You Earned it"

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.