Principal Machine Learning Engineer

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.
• Knowledge of big data processing tools such as Apache Spark.
• Proficiency in version controls systems such as Git.
• Knowledge of at least one cloud platform (e.g. AWS) and its relevant services (e.g. EMR, S3, and SageMaker).
• Ability to interpret business requirements and translate into ML deliverables.
• Ability to break down and communicate complex, highly technical concepts to audiences of varying technical understanding

Educational/Vocational/Previous Experience Recommendations:

• Bachelor degree in CS or related field required; Master’s or PhD preferred.
• 8+ years of relevant experience
• Experience writing code (e.g. Python) and taking machine learning models to production.
• Experience building software on cloud computing platforms.
• Experience deploying, monitoring, and iterating machine learning models in production.