Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4314917336&distance=25.0&f_AL=true&f_TPR=r10000&f_WT=2&geoId=103644278&keywords=machine%20learning&origin=JOBS_HOME_KEYWORD_HISTORY Job Description: Senior Machine Learning Engineer(US)  United States · Reposted 3 hours ago · Over 100 applicants Promoted by hirer · No response insights available yet $100/yr - $140/yr Remote Matches your job preferences, workplace type is Remote. Full-time Easy Apply Save Save Senior Machine Learning Engineer(US)  at Checkmate Senior Machine Learning Engineer(US) Checkmate · United States (Remote) Easy Apply Save Save Senior Machine Learning Engineer(US)  at Checkmate Show more options Your profile is missing required qualifications Show match details Help me update my profile BETA Is this information helpful? Get personalized tips to stand out to hirers Practice mock interviews personalized to every role and get custom feedback Try Premium for PKR0 Meet the hiring team Jennifer Young, PHR Jennifer Young, PHR 3rd+ Strategic HR Leader | 15+ Years Multi-State HR Experience | Change Management | HR Analytics | AI-Driven HR Operations | PHR-Certified Job poster Message About the job 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: 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. 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. 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