We’re seeking a Mid-Level Machine Learning Engineer to join our growing Data Science & Engineering team. In this role, you will design, develop, and deploy ML models that power our cutting-edge technologies like voice ordering, prediction algorithms and customer-facing analytics. You’ll collaborate closely with data engineers, backend engineers, and product managers to take models from prototyping through to production, continuously improving accuracy, scalability, and maintainability.
Essential Job Functions
• Model Development: Design and build next-generation ML models using advanced tools like PyTorch, Gemini, and Amazon SageMaker - primarily on Google Cloud or AWS platforms.
• Feature Engineering: Build robust feature pipelines; extract, clean, and transform largescale transactional and behavioral data. Engineer features like time- based attributes, aggregated order metrics, categorical encodings (LabelEncoder, frequency encoding).
• Experimentation & Evaluation: Define metrics, run A/B tests, conduct cross-validation, and analyze model performance to guide iterative improvements. Train and tune regression models (XGBoost, LightGBM, scikit-learn, TensorFlow/Keras) to minimize MAE/RMSE and maximize R².
• Own the entire modeling lifecycle end-to-end, including feature creation, model development, testing, experimentation, monitoring, explainability, and model maintenance.
• Monitoring & Maintenance: Implement logging, monitoring, and alerting for model drift and data-quality issues; schedule retraining workflows.
• Collaboration & Mentorship: Collaborate closely with data science, engineering, and product teams to define, explore, and implement solutions to open-ended problems that advance the capabilities and applications of Checkmate, mentor junior engineers on best practices in ML engineering.
• Documentation & Communication: Produce clear documentation of model architecture, data schemas, and operational procedures; present findings to technical and non-technical stakeholders.
- Academics: Bachelors/Master’s degree in Computer Science, Engineering, Statistics, or related field
- Experience:
5+ years of industry experience (or 1+ year post-PhD).
Building and deploying advanced machine learning models that drive business impact
Proven experience shipping production-grade ML models and optimization systems, including expertise in experimentation and evaluation techniques.
Hands-on experience building and maintaining scalable backend systems and ML inference pipelines for real-time or batch prediction
- Programming & Tools:
Proficient in Python and libraries such as pandas, NumPy, scikit-learn; familiarity with TensorFlow or PyTorch.
Hands-on with at least one cloud ML platform (AWS SageMaker, Google Vertex AI, or Azure ML).
- Data Engineering:
Hands-on experience with SQL and NoSQL databases; comfortable working with Spark or similar distributed frameworks.
Strong foundation in statistics, probability, and ML algorithms like XGBoost/LightGBM; ability to interpret model outputs and optimize for business metrics.
Experience with categorical encoding strategies and feature selection.
Solid understanding of regression metrics (MAE, RMSE, R²) and hyperparameter tuning.
- Cloud & DevOps: Proven skills deploying ML solutions in AWS, GCP, or Azure; knowledge of Docker, Kubernetes, and CI/CD pipelines
- Collaboration: Excellent communication skills; ability to translate complex technical concepts into clear, actionable insights.
- Working Terms: Candidates must be flexible and work during US hours at least until 6 p.m. ET in the USA, which is essential for this role & must also have their own system/work setup for remote work.
