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Machine Learning Engineer (MLOps)

Machine Learning Engineer (MLOps)

Burtch WorksWashington, DC, United States
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Join to apply for the Machine Learning Engineer role at Burtch Works

Join to apply for the Machine Learning Engineer role at Burtch Works

This range is provided by Burtch Works. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$120,000.00 / yr - $150,000.00 / yr

Job Title : Machine Learning Engineer

Location : Washington, D.C. (Hybrid - 2 days onsite)

About The Company

Our client is a global leader in AI-optimized scheduling and forecasting platforms, empowering and rewarding individuals in the fast-food and Quick Service Restaurant (QSR) industry through innovative solutions. The company fosters a dynamic startup environment, encouraging innovation, collaboration, and ownership.

Job Summary

The Machine Learning Engineer will design, train, deploy, and monitor machine learning models that address real-world customer needs. This role is central to scaling AI-powered scheduling and forecasting solutions. The position is based in Washington, DC, and reports to the Chief Analytics Officer.

Key Responsibilities

  • Build and test machine learning models to support their platform.
  • Design, build, and deploy data and ML pipelines on AWS.
  • Enable an iterative lifecycle for data products to improve, integrate, and deploy.
  • Standardize workflows, analysis, and modeling for deployment and observability in production.
  • Develop monitoring and observability systems for ML models and experiments.
  • Collaborate across teams to align modeling with engineering standards.

Requirements

  • Education : Bachelor’s or Master’s degree in a quantitative field.
  • Experience :
  • 2–4 years of relevant experience.
  • 4+ years’ experience with Python and ML frameworks.
  • 1+ year of experience with MLOps and maintaining ML models at scale.
  • Technical Skills :
  • Strong knowledge and hands-on experience with :
  • Python programming
  • SQL and relational databases; ETL processes
  • Cloud technologies (AWS, GCP, or Azure)
  • Git or other version control systems
  • Model versioning / tracking (DVC, MLFlow)
  • ML pipeline development / deployment (Metaflow, Kubeflow, Prefect, Dagster)
  • Containers (Docker, Kubernetes)
  • Visualization and monitoring tools (Dash, Streamlit)
  • Modeling / tuning / optimization with frameworks (sklearn, PyTorch)

    • Preferred Qualifications

  • Real-time inference deployment and monitoring (FastAPI, Ray Serve).
  • CI / CD practices.
  • Model deployment strategies (A / B testing, canary release).
  • Cross-functional collaboration (DevOps, Data Engineering, Data Science).
  • Time series analysis and predictive modeling.

    • Benefits

  • Salary range : $120-150K
  • Health and Wellness : Industry-best benefits.
  • Work-Life Balance : HYBRID – 2 days in office, 3 days from home.

    • Seniority level

    Seniority level

    Mid-Senior level

    Employment type

    Employment type

    Full-time

    Job function

    Job function

    Engineering and Information Technology

    Industries

    IT Services and IT Consulting

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