Role overview
The Machine Learning Engineer is responsible for designing, building, deploying, and maintaining production‑grade machine learning solutions that drive business value across the enterprise. This role sits at the intersection of software engineering and data science, with a strong focus on scalable ML systems, model lifecycle management, and integration with enterprise platforms. The ideal candidate is hands‑on, technically strong, and comfortable operating in a fast‑paced, cross‑functional environment. Key responsibilities include:
- Design, develop, train, and deploy machine learning models using supervised and unsupervised techniques (e.g., regression, classification, clustering, anomaly detection).
- Build and maintain end‑to‑end ML pipelines, including data ingestion, feature engineering, training, evaluation, and inference.
- Partner with Data Science, Data Engineering, and business stakeholders to translate requirements into scalable technical solutions.
- Implement MLOps best practices, including CI/CD, model versioning, monitoring, and retraining strategies.
- Optimize model performance, scalability, reliability, and cost efficiency in production environments.
- Integrate machine learning models into enterprise applications, APIs, and data platforms.
- Ensure data quality, model explainability, and adherence to security, governance, and compliance standards.
- Communicate complex machine learning concepts and results clearly to technical and non‑technical stakeholders.
What we're looking for
- Bachelor’s degree in Computer Science, Data Science, Engineering, or equivalent experience.
- 5+ years of experience building and deploying machine learning models in production environments.
- Must be located in the Chicago, IL surrounding area or willing to relocate for the duration of employment.
- Willingness to adapt and thrive in a blended work environment with 3-days in office, seamlessly transitioning between remote work and in-office operations.
- Proficiency in Python and experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit‑learn.
- Strong experience with AWS SageMaker for data preparation, pipelines, and model deployment.
- Experience with Git and modern software engineering best practices.
- Familiarity with SQL (including T‑SQL) and experience working with relational and geospatial databases.
- Experience with retrieval‑augmented generation or generative AI solutions is a plus.
- Understanding of Agile development practices and comfortable working in evolving, ambiguous environments.
- Must be legally authorized to work in the United States without need for employer sponsorship now or in the future.
The estimated base salary range for this position is $135,000 – $190,000 per year. This range reflects a good-faith estimate of the base salary Ventas reasonably expects to pay at the time of posting. Actual base pay will be determined based on work location, skills, qualifications, relevant experience, and business needs.
In addition to base salary, this role is eligible for discretionary incentive compensation and a comprehensive benefits package, which includes medical, dental, vision, retirement savings, paid time off, and other wellness benefits under applicable plan terms.
Ventas, Inc. offers a competitive compensation and benefits package to the successful candidate.
Ventas, Inc. is an Equal Opportunity Employer.
Ventas, Inc. does not accept unsolicited resumes from staffing agencies, search firms or any third parties.