Machine Learning Engineer

About The Role Job Title Machine Learning Engineer Location Tues/Wed on-site in Philadelphia Duration 6 month contract to hire Compensation $90/hr C2C Work Status USC/GC Notes Can be flexible on years of experience here - manager is open to seeing al options with the role being hybrid Internal Notes: This is a hands on model-building role first. The ideal candidate should be comfortable spending most of their time working directly with data, features, models, scoring logic, validation methods, production workflows, and model improvement. They should also be able to operate with the maturity of a principal-level engineer: shaping unclear problems, making pragmatic technical decisions, mentoring others, and driving work forward (proactive vs reactive). Looking for experience building predictive scores, risk scores, health scores, engagement scores, prioritization models, scorecards, or similar decision-support systems. • Not stuck on years of experience, open to someone who even has 4 if they have hands on experience building models. This is a hands on building role, must be comfortable taking data and working it into a model. This includes hands-on experience with feature engineering, model training, validation, calibration, thresholding, monitoring, production scoring, and model improvement. • Role is 70% hands on, 30% stakeholder collab/ production support. Will be building commercial software models. • Experience with modern ML and data platforms. They use Databricks, but Spark, MLflow, Snowflake etc are fine. • Strong communication, must be able to work under pressure. Very greenfield environment, models are in development. • Will be people managing, 2 onshore and 1 offshore. Fine if they don't have that experience, more focused on skillset. Status/Updates Job Description Position Description: We are looking for a 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. Key Responsibilities Hands-On Model Development Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support. Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation. Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness. Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features. Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries. Move quickly from data exploration to prototype to validated model to production-ready capability. Scoring, Scorecards, and Transparent Models Design and implement predictive scores, risk tiers, score bands, thresholds, cut points, and intervention logic. Build transparent and interpretable models where explainability is important, including logistic regression, generalized linear models, decision trees, monotonic models, calibrated models, scorecard-style models, or explainable boosting approaches. Evaluate models for accuracy, calibration, stability, drift, fairness, interpretability, and operational usefulness. Help stakeholders understand what a score represents, how it should be used, how it should not be used, and how changes in the score should be interpreted. Document model logic, features, assumptions, limitations, validation results, and recommended usage in a way that business and technical stakeholders can understand. Define the evidence needed to show that a model or score is valid, stable, explainable, actionable, and useful. Production ML and MLOps Partner with data engineering, analytics engineering, platform engineering, and application engineering teams to move models from experimentation into reliable production workflows. Support model deployment, batch scoring, real-time or near-real-time inference, model versioning, monitoring, retraining, and performance tracking. Help define data pipelines, feature pipelines, inference flows, model outputs, feedback loops, and monitoring requirements. Ensure models are observable, supportable, secure, scalable, and aligned with enterprise architecture and governance expectations. Establish practical monitoring and feedback loops to determine whether models continue to perform and create value over time. Product and Rapid-Build Execution Operate effectively in a rapid-build, startup-like environment where speed, ownership, and pragmatic decision-making are important. Turn early-stage ideas, ambiguous business needs, and rough concepts into working ML products, scores, prototypes, and production capabilities. Bring a product-engineering mindset to ML development, including user needs, workflow integration, adoption, usability, feedback loops, and measurable outcomes. Drive work forward without waiting for perfect requirements, while still identifying critical assumptions, risks, dependencies, and evidence needed before scaling. Partner with business and product stakeholders to define MVPs, iterate quickly, learn from usage, and improve models over time. Make smart tradeoffs between quick prototypes, durable platforms, transparent models, GenAI-enabled workflows, and longer-term ML architecture. Generative AI and AI Automation Support the design and development of GenAI-enabled solutions, including LLM-powered workflows, RAG, summarization, conversational agents, document intelligence, and decision-support tools. Help evaluate when GenAI is appropriate versus when traditional ML, rules, analytics, or transparent scoring models are a better fit. Partner with product, engineering, and business stakeholders to integrate predictive models, scores, and GenAI outputs into practical workflows. Apply appropriate evaluation, guardrails, monitoring, privacy controls, and human-in-the-loop processes for GenAI use cases. Help the organization balance innovation with explainability, safety, reliability, privacy, and operational usefulness. Requirement Shaping and Stakeholder Partnership Work directly with business, product, analytics, operations, and engineering stakeholders to clarify what a model is intended to predict, explain, recommend, or trigger. Translate business questions into measurable ML objectives, target variables, features, validation approaches, and success metrics. Ask practical questions early: who will use the score, what action will it inform, what does a false positive or false negative mean, and how will we know the model is creating value? Communicate model behavior, tradeoffs, limitations, and recommended usage clearly to both technical and non-technical audiences. Help the team avoid becoming an AI ticket factory by shaping solutions, not just executing requests. Principal-Level Technical Leadership Provide technical leadership through hands-on example, strong engineering judgment, and clear recommendations. Proactively identify model risks, data gaps, unclear requirements, design issues, and opportunities for improvement. Help establish practical standards for model development, validation, documentation, monitoring, and production readiness. Mentor other engineers and data scientists through code reviews, design reviews, modeling guidance, and shared best practices. Demonstrate high ownership by driving clarity, execution, and continuous improvement. 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. Success in This Role Looks Like High-quality models and scores are built, validated, deployed, monitored, and improved over time. Model outputs are explainable and trusted by business and operational stakeholders. Scores are connected to real decisions, workflows, interventions, or measurable outcomes. The organization moves faster because this person can turn ambiguity into working ML capabilities. The ML team has stronger standards for model development, validation, documentation, monitoring, and production readiness. Business partners understand what the models do, how to use them, where their limitations are, and how to interpret changes in outputs. The team avoids building models in isolation and instead builds ML capabilities that are connected to products, workflows, users, and business value. GenAI is applied thoughtfully where it improves workflow, decision support, summarization, automation, or user experience, without replacing appropriate model governance or human judgment. Key Responsibilities & Skills • Hands-On Model Development • Feature Engineering • Model Training & Validation • Model Calibration & Thresholding • Scorecard & Predictive Scoring • Explainable / Interpretable Models • MLOps & Production Deployment • Model Monitoring & Drift Detection • Stakeholder Collaboration & Communication • Product‑Mindset Rapid Prototyping • Generative AI Integration • Technical Leadership & Mentoring Technical Skills • Python / SQL • Spark / Databricks • MLflow / Snowflake • Azure / AWS • scikit-learn / XGBoost • Git / CI‑CD • Docker / Kubernetes Education Bachelor's Degree in Computer Science, Data Science, Statistics, Electrical Engineering, Machine Learning, Applied Mathematics. Preferred: Master's in Data Science, Master's in Machine Learning, PhD in Computer Science, PhD in Machine Learning, MBA. Industry Experience • Commercial Software • Healthcare / Health Tech • Financial Services • Technology Services • AI / Applied AI #CareerOpportunities #JobVacancy #WorkWithUs