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Machine Learning Engineer in Denver

Job in Denver, Denver County, Colorado, 80285, USA
Listing for: TalentBridge
Full Time position
Listed on 2026-02-28
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer, Data Engineer, Cloud Computing
Salary/Wage Range or Industry Benchmark: 150000 - 200000 USD Yearly USD 150000.00 200000.00 YEAR
Job Description & How to Apply Below
Position: Full Time Opportunity Machine Learning Engineer in Denver, CO

Role: Senior Machine Learning Engineer
Location: Denver CO (Hybrid)
Type: Fulltime

Role Summary

The Machine Learning Engineer is responsible for implementing, deploying, and maintaining machine learning models in a cloud-based ML platform. This role serves as a subject matter expert in Machine Learning Operations (MLOps), bridging the gap between data science and production-grade systems. The engineer will help shape and guide ML solutions within an evolving technology stack and will have the autonomy to recommend and implement best-practice approaches.

Required

Education & Experience
  • Bachelor’s degree (Master’s preferred) in Statistics, Mathematics, Computer Science, or a related quantitative field.
  • 7+ years of experience in data science or a related discipline.
  • 3+ years of hands‑on experience with MLOps and production ML systems.
  • Proven experience deploying and scaling machine learning models in production environments.
  • Strong programming skills in Python and cloud automation/scripting.
  • Experience with big data platforms, real‑time/streaming data, and distributed or cluster computing.
  • Hands‑on knowledge of cloud platforms, particularly AWS.
Key Responsibilities
  • Implement and operationalize data science models in a cloud-based ML platform (e.g., AWS Sage Maker).
  • Design and maintain systems to monitor model performance, reliability, and drift in production.
  • Act as the MLOps subject matter expert, advising data scientists on model design and deployment considerations.
  • Collaborate with data engineering teams to build and maintain data pipelines from enterprise data sources (e.g., Snowflake, time‑series systems).
  • Partner with architecture teams to ensure compute, networking, and endpoint requirements are incorporated into ML solutions.
  • Stay current with emerging machine learning techniques, tools, and best practices, and apply them where appropriate.
  • Work effectively within a geographically distributed team, communicating priorities and project status clearly.
  • Design solutions that balance performance, scalability, and cost to meet business objectives.
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