Machine Learning Engineering Manager

Join Clio, a rapidly growing engineering organization, as a Machine Learning Engineering Manager. In this role, you will lead a team of ML engineers, design and ship agentic systems, collaborate cross-functionally, and participate in diverse projects. You will have access to specialized learning and development programs, equitable compensation, company equity, flexible paid time off, parental leave options, and various health benefits.

• Lead a team of ML engineers to design and ship state-of-the-art AI solutions, including traditional ML models, GenAI, and agentic AI.
• Collaborate cross-functionally with MLOps engineering, product management, operations, and data science to identify new tooling for ML and LLM-driven features.
• Participate in diverse projects and collaborate with multiple engineering teams across three countries, providing mentorship and guidance.
• Demonstrated success in mentorship in software development, particularly using an Agile process and with large scale SaaS products
• Clear and concise communication skills and the ability to build high-trust relationships with fellow Clions and customers
• Experience to manipulate, clean, and pre-process complex unstructured data for model development
• In-depth understanding of LLMs, GenAI, and the competitive landscape, including where the technology is heading
• A history of past projects (including notable successes and lessons learned)
• Experience in managing high performing teams
• Experience in fine-tuning foundational models and/or training language models in-house
• The ability to become fluent in new technologies quickly and work effectively in an ever-evolving environment that includes distributed teams and customers
• Hands-on familiarity with building agentic systems, such as tool-calling agents, RAG pipelines, prompt and context design, and evaluating agent behavior at scale
• A diverse base of knowledge that allows you to help your team solve complex technical problems
• Experience with technical evaluations of various ML and LLM products, vendors, out-of-the-box solutions, and conducting quick proof of concepts if necessary