Machine Learning Engineer

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Job Description

Job Description

Lifelenz is a fast-growing startup building its U.S. base in Chicago. Our AI-powered platform helps global brands like McDonald's optimize labor forecasting, compliance, and scheduling while giving frontline workers more control over their pay, hours, and lives.

The role of the LIFELENZ Machine Learning Engineer is to aid in the designing, training, experimenting, production deployment, and monitoring of machine learning models that are developed in alignment with our mission - to build and grow a global AI-optimized scheduling and forecasting platform that will empower and reward people within the fast-food and Quick Service Restaurant (QSR) industry.

LIFELENZ ML Engineers have a passion for understanding the modeling needs that solve real customer problems and devising innovative solutions to deploy, monitor, and iteratively improve modeling solutions at scale.

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a relevant field with 2-4 years of experience.
  • 1+ year experience with MLOps and maintaining machine learning model lifecycle at scale
  • Strong knowledge and hands-on experience building or maintaining several of the following MLOps areas:
    • Versioning and Tracking Models and Experiments (e.g. DVC, MLFlow)
    • Iterative ML Pipeline Development and Deployment (e.g. Metaflow, Airflow, Prefect, Dagster)
  • Container Applications (eg. Docker, Kubernetes)
  • Visualizing ML processes (eg. Dash, Streamlit). Monitoring and debugging large amounts of models in production, maintaining observability and explainability of active ML processes

Preferred Qualifications

  • Experience with the following:
    • Real time inference deployment and monitoring (e.g. FastAPI, Ray Serve)
    • CI/CD practices
    • Model Deployment Strategies (e.g. A/B testing, canary release)
    • Cross team projects (DevOps, Data Engineering, Data Science)

Salary: $110,000-$150,000

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Location:
Washington
Category:
Manufacturing

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