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. Some subjective notes: This is our first Machine Learning hire.

We are looking for a senior level engineer who can help guide us in this implementation.

We are looking for someone with clear verbal and written communication skills, who can make their presence felt in meetings with C level executives.

We are looking to learn from this person on what is the best method to implement machine learning across our organization, as we are looking at ML as a layer that will run across multiple products in our organization.

This person will coordinate closely with the product person, other engineers and C level execs to shape the product and also drive implementation. To specify again, this is a senior level role but in an IC capacity and will grow into a role that will build a team under him / her. Essential Job FunctionsModel 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 fieldExperience: 5+ years of industry experience (or 1+ year post-PhD)Building and deploying advanced machine learning models that drive business impactProven 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 predictionProgramming 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 pipelinesCollaboration: 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.