About The Role
The Machine Learning Engineer owns the full lifecycle of production ML systems, from data preparation and model development through deployment, monitoring, and iteration. The work spans supervised learning, ranking, forecasting, and NLP use cases where accuracy, inference latency, and operational reliability all matter.
Based in Denver, CO with a remote work structure, the role partners with data scientists, software engineers, and platform teams to turn research prototypes into dependable services. The team uses Python, PyTorch or scikit-learn, cloud infrastructure, and modern MLOps practices to deliver measurable product and business outcomes.
Key Responsibilities
- Design, train, and evaluate machine learning models for classification, recommendation, ranking, forecasting, and NLP applications
- Build reproducible data and feature pipelines using Python, SQL, Spark, or equivalent distributed processing tools
- Deploy and serve models through AWS SageMaker, Vertex AI, Kubernetes, or comparable cloud platforms with clear versioning and rollback processes
- Develop model APIs and batch inference workflows that meet defined requirements for throughput, latency, scalability, and reliability
- Implement monitoring for data quality, feature drift, model performance, service health, and operational regressions
- Collaborate with data scientists and software engineers to productionize experiments, improve evaluation methodology, and resolve model performance issues
- Write tested, maintainable code and contribute to technical design reviews, CI/CD workflows, documentation, and ML engineering standards
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 PyTorch, TensorFlow, scikit-learn, or comparable ML frameworks
- Solid understanding of machine learning fundamentals, including feature engineering, model selection, regularization, cross-validation, and evaluation metrics
- Experience building data pipelines with SQL and tools such as Spark, Airflow, dbt, or equivalent workflow and orchestration technologies
- Practical experience with cloud infrastructure and MLOps practices, including Docker, Kubernetes, CI/CD, model registry, experiment tracking, and monitoring
- Bachelor’s or master’s degree in computer science, machine learning, statistics, engineering, or a related technical field
- Bonus: Experience with LLMs, embeddings, vector search, real-time inference, feature stores, causal modeling, or distributed model training