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.