Machine Learning Engineer

Focus Financial Partners•Boston, MA

1d•$140,000 - $180,000

About The Position

We are seeking a skilled Machine Learning Engineer with approximately three years of hands-on experience designing, deploying, and maintaining production-grade machine learning systems. In this role, you will collaborate closely with data scientists, software engineers, and product teams to translate research models into reliable, scalable, and high-impact applications. You will be deeply involved in the end-to-end ML lifecycle—from data ingestion and feature engineering to deployment, monitoring, and continuous improvement—playing a critical part in shaping our machine learning platform and capabilities. This role can be located in St. Louis, MO; Boston, MA.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
3+ years of experience in machine learning engineering, applied ML, or related software engineering roles.
Strong proficiency in Python and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
Experience with distributed data processing and compute frameworks (e.g., Pandas, Spark, Dask).
Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
Familiarity with CI/CD pipelines, testing automation, and version control using Git.
Strong understanding of model evaluation, feature engineering, and performance optimization in production contexts.
Excellent analytical, communication, and collaboration skills, with the ability to work effectively in cross-functional teams.

Nice To Haves

Experience working with cloud-based ML platforms or services (e.g., SageMaker, Vertex AI, Databricks, or Snowflake ML) is preferred.

Responsibilities

Develop, deploy, and optimize machine learning models for real-world business use cases and client-facing applications.
Partner with data scientists to operationalize predictive models and ensure scalable, maintainable, and performant production deployments.
Design and implement data pipelines and workflows that support training, inference, and model lifecycle management.
Work with large, complex datasets to ensure data quality, reproducibility, and reliable version control across ML workflows.
Implement model monitoring, logging, and alerting strategies to track performance, detect drift, and support retraining cycles.
Leverage cloud platforms (AWS, Azure, GCP) to build scalable ML solutions using managed services and infrastructure-as-code practices.
Write clean, modular, and well-documented code aligned with MLOps and software engineering best practices.
Stay current on emerging ML tooling, frameworks, and industry best practices to continuously enhance our platform and capabilities.