Role Description The Associate Machine Learning Engineer supports the development, testing, and deployment of machine learning models that enhance healthcare operations and patient outcomes at WVU Medicine. Working under the guidance of senior engineers and data scientists, this role applies foundational machine learning and software engineering skills to build, evaluate, and deploy ML solutions. This position contributes to CI/CD pipelines, assists with model deployment and monitoring, and supports the integration of Generative AI and large language model (LLM) capabilities into enterprise systems. Follows established engineering and MLOps practices while developing skills in building production-grade systems. The Associate Machine Learning Engineer works with a variety of healthcare data sources, including electronic medical records, claims data, and unstructured data, to support the delivery of reliable, production-ready solutions. This role is suited for early-career professionals with a strong foundation in machine learning concepts, experience programming in Python, and an interest in applying AI within healthcare. Qualifications - Bachelor’s degree in Machine Learning, Computer Science, Mathematics, Data Science, or related field AND one (1) year of professional experience in machine learning or software engineering (internships, academic research, and capstone projects qualify). - Master’s degree in a related field with no prior professional experience required. - Certifications or coursework related to machine learning, data science, or software engineering. - Hands-on experience with machine learning and LLM (large language model) frameworks. Requirements - Support data collection, cleaning, preprocessing, and feature engineering for ML model development. - Assist in training, evaluating, and validating machine learning models using Python and standard ML frameworks. - Write and maintain Python scripts and modules that are clean, documented, and production-oriented. - Contribute to CI/CD pipeline development and maintenance for automated model training, testing, and deployment in on-premise Linux-based environments. - Build and manage Docker containers for packaging ML models and dependencies for on-premise deployment on Linux-based servers. - Support productionalization of ML models, ensuring models are scalable, reliable, and ready for deployment at scale. - Assist in the integration and testing of Generative AI and LLM-powered features into healthcare applications and workflows. - Document model development processes, data pipelines, and deployment steps clearly and thoroughly. - Participate in Agile sprint ceremonies including standups, sprint planning, and retrospectives. - Collaborate with data scientists, ML engineers, and software engineers across functional teams. - Monitor deployed models for performance drift using available observability tools and contribute to retraining and improvement cycles. - Stay current with advancements in ML, Generative AI, LLMs, and open-source tools and frameworks. Benefits - Scheduled Weekly Hours: 40 - Shift: Exempt/Non-Exempt: United States of America (Exempt) Company Description West Virginia University Health System and its subsidiaries (collectively "WVUHS") is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. WVUHS strictly prohibits and does not tolerate discrimination against employees, applicants, or any other covered persons because of race, color, religion, creed, national origin or ancestry, ethnicity, sex (including gender, pregnancy, sexual orientation, and gender identity), age, physical or mental disability, citizenship, past, current, or prospective service in the uniformed services, genetic information, or any other characteristic protected under applicable federal, state, or local law. All WVUHS employees, other workers, and representatives are prohibited from engaging in unlawful discrimination. This policy applies to all terms and conditions of employment, including, but not limited to, hiring, training, promotion, discipline, compensation, benefits, and termination of employment.

• Design and ship the next generation ranking and ML backend systems that power discovery of Dev Platform apps and games across Reddit surfaces. • Partner with ML, DS, and product to define signals, APIs, and feedback loops. • Explore and productionize new retrieval, ranking, and experimentation approaches. • Champion reliability, observability, and experimentation best practices. • Write efficient, scalable code in our Go/Python/Baseplate/GraphQL stack. • Mentor engineers and lead technical discussions.

• Design User Understanding Strategy: Define a unified user understanding framework and strategy: how users are represented (embeddings, tags, attributes, LLM-based user profile), how they are computed, stored, and exposed. Provide thought leadership in user understanding and user modeling by setting a long-term technical vision and advancing the state-of-the-art in the field. • Build Foundational User Models: Lead design and implementation of advanced user models, e.g. large-scale user representation learning (sequence-based, multi-interest, multi-task) that share representations across surfaces to improve personalization experience across key Reddit products e.g. Feeds, Notification, Search and Ads, balancing latency, cost, and performance. • Reimagine user understanding with LLM/Gen-AI: Evolve user modeling beyond traditional representations by leveraging LLMs to build richer user understanding (e.g., dynamic user profiles, intent inference, semantic reasoning over user behavior). Explore how LLMs can augment or unify embeddings, attributes, and taxonomies to enable more adaptive, interpretable, and context-aware personalization. • Ship Large Scale User Understanding as a System: Partner with platform teams to design and build core components for large-scale learning and serving: storage/retrieval for embeddings, feature pipelines, and APIs. Collaborate with ML/Ranking infra to ensure low-latency serving, high availability, and integration with MLOps systems. • Drive Cross-Team Integration & Impact: Partner with Feeds, Notification, Search and Ads teams to drive experimentation and adoption of new user understanding models with product teams across Reddit, ensuring measurable end-to-end impact on key metrics. • Set Technical Bar & Mentor: Mentor senior to staff engineers, lead design reviews, steward technical decisions across the user understanding domain, and champion and drive engineering processes and best practices

• Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes such as fraud loss reduction, approval rates, and customer experience • Lead a team of machine learning engineers to design, build, and iterate on high-impact fraud models across the full ML lifecycle, from experimentation to production • Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data • Partner cross-functionally with Product, Fraud Analytics, Risk, and Engineering to define solutions, evaluate trade-offs, and ensure models are effectively integrated into decisioning systems • Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership