Senior Machine Learning Engineer

About Checkmate

Checkmate is a restaurant technology solution provider that has continually evolved over time. We started in 2017 by integrating 3rd party platforms to the POS systems of restaurants. At that time, there were multiple 3rd party platforms like GrubHub, UberEats, DoorDash, Postmates, Caviar, and even Amazon!

This was the photograph that started it all!

We have since then continually evolved to add multiple products to our portfolio, the primary ones being first party ordering solutions like web and app ordering, kiosks and catering. We have now recently moved into three new exciting products: Digital Menu Boards, Phone Ordering AI and Drive thru AI. We form a very core part of the restaurant technology ecosystem, and are continually adding more and more digital solutions for the restaurant brands to increase their sales.

Our revolutionary enterprise menu management system, Everyware, truly unlocks the potential of menus and how it can be customized for each individual digital channel. As you can see, this is a company that continually evolves and adapts and today we are powering digital ordering solutions for some of the largest brands in the world.

We have been called the "north star of vendors" as we truly believe that technology is just a method by which we service the customers, it does not form the entirety of it. Service is a big component of what we provide to our customers, which is inherently believed by every single team member here. We are doing a lot of exciting things, including application of AI in our products and systems, using experimentation at scale to determine what works for our clients and ML to analyze and productize the massive amount of data we have. Each individual here makes a difference and has a valuable contribution. Key traits here are ownership and drive. Join us if you think you have them.

About the Role:

We’re seeking a Senior-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 large-scale 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.

Requirements

• 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.
• Must be comfortable working in US hours at least till 5 pm EST.

Preferred Qualifications

• Master’s or advanced degree in Computer Science, Engineering, Statistics, or related field.
• Familiarity with data-privacy regulations (GDPR, CCPA) and best practices in secure ML.
• Open-source contributions or publications in ML/AI conferences.
• Experience with Ruby on Rails programming framework