Senior Machine Learning Engineer(US)

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 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.

100 % Remote

$100,000 to $140,000

Requirements

Academics : Bachelors / Master’s degree in Computer Science, Engineering, Statistics, or related field

Experience :

Programming & Tools :

Data Engineering :

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.

Preferred Qualifications

Benefits