Principal Machine Learning Engineer
ConnectWise is an industry and Global leading software company with over 3,000 colleagues in North America, EMEA and APAC. As a community-driven software company dedicated to the success of technology solution providers, our suite helps over 45,000 of our partners manage their businesses better, sell more efficiently, automate service delivery, and remotely control technology so they can consistently deliver amazing customer experiences. Our company is powered by our connections, our colleagues, and our community. And, we accept all kinds. Game-changers, innovators, culture-lovers—and humankind. We invite discovery and debate. We recognize key moments as milestones. We see you and value you for your unique contributions. Our inclusive, positive culture lays the foundation to ensure every colleague is valued for their perspectives and skills, giving you the choice of how YOU make a difference. Curious? Read this opportunity to learn how YOU can make a difference at ConnectWise! General Summary: The Principal Machine Learning Engineer is responsible for building Machine learning models based on diverse business requirements, setting up the pipelines, and assisting in delivering thoughtful experiences for our partners. This role works in partnership with cross-functional teams to contribute to the development of cutting-edge ML solutions. Essential Duties and Responsibilities: Builds/ Optimizes machine learning models. Researches, analyzes, and documents findings. Assists in delivering production grade machine learning services that power the ConnectWise platform and products. Designs and maintains machine learning infrastructure. Informs, influences, supports, and executes on product decisions and product launches. Works with cross-functional teams to ensure that proper data pipelines are established to ensure availability of high-quality data. Knowledge, Skills, and/or Abilities Required: Ability to work independently on projects and processes with close supervision. Broad theoretical knowledge of ML/ AI space and application development using generative AI including supervised fine-tuning, preference optimization (DPO), and reinforcement fine-tuning (RFT) of LLMs; parameter-efficient fine-tuning (LoRA/QLoRA); fine-tuning encoder models such as BERT/ModernBERT for text classification; and retrieval-augmented generation with embedding retrievers and cross-encoder rerankers. Strong grasp of model evaluation methodology (task-specific eval sets, LLM-as-judge, offline metrics, and online A/B testing) and experience building training-data, synthetic-data, and distillation pipelines for post-training. Ability to situationally adapt and understand new technology/processes as per business partner requirement. Strong programming skills in python and fluency in common libraries ( Hugging Face Transformers, TRL, PEFT, Sentence-Transformers, scikit-learn, etc.) Proficient in SQL and/or other data manipulation languages. Know...