Role overview
Job Title:
Machine Learning Engineer (MLOps)
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.
What you'll work on
- 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.
What we're looking for
- 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.