Senior Machine Learning Engineer - Data Platform

Qventus is a leader in healthcare transformation, focusing on patient care through innovative machine learning solutions. The Senior Machine Learning Engineer will be responsible for productionalizing and scaling machine learning models, collaborating with Data Scientists to ensure the accuracy and reliability of AI systems used in hospitals.

Responsibilities:

• Build, run, and evolve production ML and LLM systems by implementing feature pipelines, training and retraining workflows, and batch and real-time inference on top of Qventus’ data platform
• Monitor and optimize model performance across hospitals, improving accuracy, latency, cost, and reliability
• Build and maintain model-level feature pipelines and feature management systems on top of curated datasets to support training, inference, and replay
• Collaborate with Data Science leaders to establish best practices for applied ML at Qventus, setting standards for feature design, evaluation, and production readiness through iteration and retraining

Requirements:

• 3+ years building and running machine learning models in production using Python and SQL in modern cloud-based ML environments (AWS & Databricks preferred) & ML frameworks (e.g., scikit-learn, PyTorch, XGBoost, TensorFlow, or HuggingFace)
• Demonstrated ability to design and run feature engineering, training, and inference workflows in applied ML systems
• Familiarity with operationalizing LLMs or retrieval-augmented generation (RAG) systems; Exposure to LLM frameworks and libraries (Langchain, LlamaIndex, HuggingFace, etc.)
• Strong understanding of software engineering principles and writing maintainable, modular code
• Strong collaboration and communication skills — able to partner closely with product, clinical, and engineering stakeholders
• 3+ years applied or research experience using a wide variety of statistical and machine learning techniques - particularly in NLP, explainable ML (Python)
• Experience supporting cloud-based, highly available, observable, and scalable data platforms utilizing large, diverse data sets in production to meet ambiguous business needs
• Strong background in data quality validation and model monitoring in healthcare or regulated environments
• Experience supporting ML Ops infrastructure (model packaging, orchestration, observability, CI/CD)
• Prior experience working in healthcare, particularly with EMR, claims, or hospital operations data
• Master's degree in Computer Science, Engineering, or related field, or equivalent industry experience