Senior Machine Learning Engineer

Checkmate

114d • $100,000 - $140,000

About The Position

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.

Requirements

• Bachelors/Master’s degree in Computer Science, Engineering, Statistics, or related field.
• 5+ years of industry experience (or 1+ year post-PhD).
• Proven experience shipping production-grade ML models and optimization systems.
• Hands-on experience building and maintaining scalable backend systems and ML inference pipelines for real-time or batch prediction.
• 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).
• 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.
• Solid understanding of regression metrics (MAE, RMSE, R²) and hyperparameter tuning.
• Proven skills deploying ML solutions in AWS, GCP, or Azure; knowledge of Docker, Kubernetes, and CI/CD pipelines.
• Excellent communication skills; ability to translate complex technical concepts into clear, actionable insights.

Nice To Haves

• 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.

Responsibilities

• Design and build next-generation ML models using advanced tools like PyTorch, Gemini, and Amazon SageMaker - primarily on Google Cloud or AWS platforms.
• Build robust feature pipelines; extract, clean, and transform large-scale transactional and behavioral data.
• 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.
• Implement logging, monitoring, and alerting for model drift and data-quality issues; schedule retraining workflows.
• Collaborate closely with data science, engineering, and product teams to define, explore, and implement solutions to open-ended problems.
• Mentor junior engineers on best practices in ML engineering.
• Produce clear documentation of model architecture, data schemas, and operational procedures.
• Present findings to technical and non-technical stakeholders.

Benefits

• Health Care Plan (Medical, Dental & Vision)
• Retirement Plan (401k)
• Life Insurance (Basic, Voluntary & AD&D)
• Flexible Paid Time Off
• Family Leave (Maternity, Paternity)
• Short Term & Long Term Disability
• Training & Development
• Work From Home
• Stock Option Plan

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Bachelor's degree