Principal Machine Learning Engineer (W2 Only)

• Title: Principal Machine Learning Engineer
• Duration: 6 Mos C2H
• Location: Hybrid in Philadelphia, PA onsite Tue & Wed each week (Local candidates preferred but, those willing to relocate are acceptable)

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

• Required Qualifications
• Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.
• 5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.
• 3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.
• Strong hands-on experience with Python and SQL.
• Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.
• Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.
• Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.
• Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.
• Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.
• Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

• Scoring, Scorecards, and Transparent Models
• Production ML and MLOps
• Product and Rapid-Build Execution
• Generative AI and AI Automation
• Requirement Shaping and Stakeholder Partnership