About The Role
The Machine Learning Engineer will build, deploy, and operate production ML systems for high-volume products, translating research prototypes into reliable services. The work spans model development, feature pipelines, inference APIs, and monitoring across structured, behavioral, and unstructured data.
Based in Chicago, IL with a remote work model, the role partners with applied scientists, data engineers, and platform teams to improve model quality, latency, and operational reliability. Success means shipping models that perform consistently in production and can be measured, debugged, and improved over time.
Key Responsibilities
- Design, train, and evaluate supervised and unsupervised models using Python, scikit-learn, PyTorch, or TensorFlow for ranking, classification, forecasting, or recommendation use cases
- Build reproducible feature engineering and training pipelines with SQL, Spark, Airflow, or equivalent workflow orchestration tools
- Deploy models as scalable inference services using Docker, Kubernetes, AWS SageMaker, Vertex AI, or comparable cloud infrastructure
- Implement model versioning, experiment tracking, CI/CD, and automated validation with tools such as MLflow, GitHub Actions, or equivalent platforms
- Monitor production systems for latency, data drift, feature quality, model degradation, and service reliability using metrics, logs, and alerting
- Collaborate with data scientists and software engineers to review designs, investigate model failures, and deliver tested, documented ML components
What We Are Looking For
- 3–8 years of experience in machine learning engineering, applied machine learning, or a closely related software engineering role, including production model deployment
- Strong Python skills and hands-on experience with at least one major ML framework such as PyTorch, TensorFlow, or scikit-learn
- Practical knowledge of model development fundamentals, including feature engineering, validation strategies, hyperparameter tuning, calibration, and metric selection
- Experience building data and ML pipelines with SQL and at least one distributed processing or orchestration tool such as Spark, Airflow, or Databricks
- Proficiency with cloud infrastructure and production engineering practices, including Docker, Kubernetes, REST or gRPC services, testing, and CI/CD
- Bachelor’s or master’s degree in computer science, engineering, statistics, mathematics, or a related quantitative discipline
- Bonus: Experience with LLMs, embeddings, retrieval-augmented generation, real-time inference optimization, model observability, or GPU-based deployment