Senior ML Engineer

Have you ever found yourself or a loved one waiting hours and hours in a hospital Emergency Room to get care? Or have you ever had a surgery scheduled for months in the future that needed to happen sooner? Unfortunately, our healthcare system is full of these types of operational problems. Our work saves lives and helps hospitals cut tens of millions of dollars in operational costs, while improving the quality of care they’re able to deliver.

Qventus is a real-time decision making platform for hospital operations. Our mission is to simplify how healthcare operates, so that hospitals and caregivers can focus on delivering the best possible care to patients. We use artificial intelligence and machine learning to create products that help nurses, doctors, and hospital staff anticipate issues and make operational decisions proactively.

Qventus works with leading public, academic and community hospitals across the United States. The company was recognized by the 2019 Black Book Awards in healthcare for patient flow and by CB Insights as a 2019 top 100 Most Promising Company in Artificial Intelligence. In 2020, Qventus won the Robert Wood Johnson Foundation Emergency Response for the Healthcare System Innovation Challenge through its work helping health systems across the country plan for and operate in the COVID pandemic.

Qventus is looking for a Senior Machine Learning Engineer with a strong ML Ops focus to build and scale our AI-powered healthcare solutions. This role is ideal for someone who enjoys working across the ML lifecycle — from experimentation and feature engineering to deployment, monitoring, and iteration — with a strong focus on productionization and platform reliability. Our Data Science team supports everything from ML-based operational predictions to LLM-based assistants and spans the full machine learning lifecycle — from training and evaluation, to model deployment, versioning, and monitoring. 

As a Senior ML Engineer in the Data organization, you’ll work closely with data scientists, data engineers, and platform partners to design, deploy, and support the infrastructure behind our production ML and LLM models. You’ll lead efforts in packaging, monitoring, and scaling models across critical healthcare workflows, while also contributing to experimentation and development as needed. Your work will help ensure our AI systems are reliable, reproducible, and ready for real-world impact — enabling care teams to make better, faster decisions across the hospital. You will be strongly motivated to have an impact in the company and dedicated to helping improve the quality of healthcare and patient outcomes.

Key Responsibilities

• Build and support end-to-end ML pipelines for training, validation, deployment, and monitoring of traditional ML and LLM-based solutions

• Partner with data scientists to productionize models with a focus on reproducibility, observability, and runtime efficiency

• Manage ML infrastructure, including experiment tracking, model versioning, and deployment tooling

• Contribute to the design and implementation of scalable, low-latency model inference and batch prediction systems

Key Qualifications

• 3+ years of experience developing and maintaining machine learning systems in production environments (Python, SQL)

• 1+ years experience supporting ML Ops infrastructure (model packaging, orchestration, observability, CI/CD)

• Hands-on experience with model lifecycle tools such as MLflow, SageMaker, or similar

• 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

• Practical experience with the following tools & services: AWS services (Lambda, S3, RDS, CloudWatch), Databricks, Spark, Terraform (Atmos)

• Strong collaboration and communication skills — able to partner closely with product, clinical, and engineering stakeholders

Nice to Have

• 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

• Ability to contribute to feature engineering or algorithm tuning in partnership with domain experts

• Prior experience working in healthcare, particularly with EMR, claims, or hospital operations data

• <