Key skills
Machine LearningLarge Language Models (LLMs)Generative AI (GenAI)Agentic SystemsRetrieval-Augmented Generation (RAG) PipelinesPrompt and Context DesignModel Fine-TuningLanguage Model TrainingData Manipulation and Preprocessing for Unstructured DataAgile Software DevelopmentSaaS Product DevelopmentAIMLGenAILLMRAGAgenticMLOpsSaaSAgileProduct ManagementCommunication
About this role
Clio is the global leader in legal AI technology, empowering legal professionals and law firms of every size to work smarter, faster, and more securely. We are currently seeking a Manager, Machine Learning Engineering to join our rapidly growing engineering organization, responsible for leading a team of ML engineers and collaborating across various functions to deliver innovative AI solutions for legal professionals.
Responsibilities:
- Lead a team of ML engineers to bring state of the art AI to Clio's clients, spanning traditional ML models, GenAI, and agentic AI
- Guide the team in designing and shipping agentic systems, including retrieval, tool use, orchestration, and the evaluation frameworks that keep them reliable and safe in production
- Collaborate cross-functionally with MLOps engineering, product management, operations, and data science to identify new tooling for ML and LLM-driven features for Clio customers
- Work in an agile environment with our team of ML engineers, ML ops, and full stack developers across a variety of projects
- Learn new things, challenge yourself, and hone your craft as an ML and infrastructure expert in a space that is moving fast
- Participate in diverse projects and collaborate with multiple engineering teams across three countries
- Review and provide feedback on code, both from within your own team or across all of Clio
- Collaborate with teams across Clio to diagnose, understand, and solve problems, and to build solutions that may span many areas
- Teach and learn from those around you, providing constructive feedback and taking on feedback to help grow
Requirements:
- Lead a team of ML engineers to bring state of the art AI to Clio's clients, spanning traditional ML models, GenAI, and agentic AI
- Guide the team in designing and shipping agentic systems, including retrieval, tool use, orchestration, and the evaluation frameworks that keep them reliable and safe in production
- Collaborate cross-functionally with MLOps engineering, product management, operations, and data science to identify new tooling for ML and LLM-driven features for Clio customers
- Work in an agile environment with our team of ML engineers, ML ops, and full stack developers across a variety of projects
- Learn new things, challenge yourself, and hone your craft as an ML and infrastructure expert in a space that is moving fast
- Participate in diverse projects and collaborate with multiple engineering teams across three countries
- Review and provide feedback on code, both from within your own team or across all of Clio
- Collaborate with teams across Clio to diagnose, understand, and solve problems, and to build solutions that may span many areas
- Teach and learn from those around you, providing constructive feedback and taking on feedback to help grow
- Experience in managing high performing teams
- Experience with technical evaluations of various ML and LLM products, vendors, out-of-the-box solutions, and conducting quick proof of concepts if necessary
- In-depth understanding of LLMs, GenAI, and the competitive landscape, including where the technology is heading
- Hands-on familiarity with building agentic systems, such as tool-calling agents, RAG pipelines, prompt and context design, and evaluating agent behavior at scale
- Experience in fine-tuning foundational models and/or training language models in-house
- Experience to manipulate, clean, and pre-process complex unstructured data for model development
- The ability to become fluent in new technologies quickly and work effectively in an ever-evolving environment that includes distributed teams and customers
- Demonstrated success in mentorship in software development, particularly using an Agile process and with large scale SaaS products
- A diverse base of knowledge that allows you to help your team solve complex technical problems
- A history of past projects (including notable successes and lessons learned)
- Clear and concise communication skills and the ability to build high-trust relationships with fellow Clions and customers