Senior Machine Learning Engineer - Data Platform

Qventus•Mountain View, CA

About The Position

On this journey for over 12 years, Qventus is leading the transformation of healthcare. We enable hospitals to focus on what matters most: patient care. Our innovative solutions harness the power of machine learning, generative AI, and behavioral science to deliver exceptional outcomes and empower care teams to anticipate and resolve issues before they arise. Our success in rapid scale across the globe is backed by some of the world's leading investors. At Qventus, you will have the opportunity to work with an exceptional, mission-driven team across the globe, and the ability to directly impact the lives of patients. We’re inspired to work with healthcare leaders on our founding vision and unlock world-class medicine through world-class operations. #LI-JB1 Qventus is looking for a Senior Machine Learning Engineer to productionalize, operate, and scale machine learning models and advanced feature pipelines developed by our Data Science team across our AI-driven healthcare products. This role is ideal for someone who likes owning end-to-end model execution in production. From curated data inputs and feature computation through training jobs, batch/real-time inference, and performance iteration. As Qventus’ first dedicated Senior ML Engineer, you’ll work at the intersection of Data Science, Data Engineering, and Product to take our newest and most complex models out of notebooks and into durable, scalable production systems. You will partner with Data Scientists who develop the models and build the feature pipelines, training and retraining workflows, and batch and real-time inference logic required to run them reliably on top of Qventus’ data platform - while optimizing for accuracy, latency, cost, and stability across diverse hospital environments. Your work will ensure Qventus’ AI systems are accurate, explainable, and safe for real-world use, enabling care teams to make better, faster decisions across the hospital. You will be strongly motivated to have impact in the company and dedicated to improving the quality of healthcare and patient outcomes.

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
Hands-on experience with modern
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

Nice To Haves

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

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