Senior Machine Learning Engineer

Quantori is seeking a Senior Machine Learning Engineer to build and improve production ML pipelines and reusable ML infrastructure. The role focuses on traditional ML and scheduled batch predictions, working closely with data scientists to productionize models and automate workflows.

Responsibilities:

• Design and build end-to-end ML pipelines covering feature engineering, model training, backtesting, validation, reporting, and batch inference
• Convert data science prototypes into reliable and maintainable production solutions
• Develop a configurable ML framework using reusable components and parameter-driven workflows
• Build and maintain Python-based ML workflows using an existing codebase
• Support data preparation and transformation workflows in Snowflake and dbt Cloud
• Implement data quality checks, automated testing, versioning, monitoring, and CI/CD
• Integrate model outputs into downstream business systems and operational workflows
• Contribute to architecture decisions and propose practical implementation approaches
• Support production operations, troubleshooting, model refreshes, and continuous improvement
• Collaborate with data scientists, data engineers, architects, and business stakeholders

Requirements:

• 6–8 years of experience in machine learning engineering, software engineering, data engineering, or a related field
• Strong Python software engineering skills, including experience with reusable and object-oriented code
• Experience building production ML pipelines
• Hands-on experience with traditional ML methods and libs (pandas, NumPy, scikit-learn, PyTorch)
• Hands-on experience with Docker
• Strong SQL and data engineering skills
• Practical experience with Snowflake and dbt Cloud
• Experience working with AWS
• Experience with Git-based CI/CD workflows, such as GitHub Actions
• Understanding of testing, monitoring, versioning, reproducibility, and production support
• Strong communication, ownership, troubleshooting, and problem-solving skills
• Experience with scheduled batch prediction pipelines
• Experience building configurable frameworks that support multiple products or business units
• Familiarity with CRM or other downstream business-system integrations
• Familiarity with Kubernetes, workflow orchestration, or infrastructure as code
• Exposure to generative AI or LLM-based applications
• Experience in pharmaceutical, biotechnology, healthcare, or life sciences environments