Machine Learning Engineer (US)

Experience: 2–8 YearsLocations: San Francisco, Seattle, New York City, BostonSalary: $120,000–$205,000+ per yearEmployment Type: Full-TimeJob OverviewWe are looking for a talented Machine Learning Engineer to design, develop, deploy, and optimize machine learning solutions that solve complex business and technical problems. The ideal candidate has strong experience with Python, machine learning, data processing, and MLOps, along with the ability to take ML models from experimentation through production.Key ResponsibilitiesDesign, build, train, and evaluate machine learning models for real-world applications. Develop scalable ML pipelines for data preparation, model training, validation, and deployment. Work with large datasets to identify patterns, generate insights, and improve model performance. Implement and maintain production-grade machine learning systems. Develop automated MLOps workflows for model deployment, monitoring, versioning, and retraining. Collaborate with data scientists, software engineers, data engineers, and product teams. Optimize models for accuracy, scalability, latency, and reliability. Monitor production models and identify model/data drift and performance issues. Write clean, maintainable, and well-tested Python code. Research and evaluate new machine learning techniques, frameworks, and tools. Contribute to technical architecture and best practices for ML systems. Required Skills2–8 years of experience in machine learning engineering, data science, or a related field. Strong proficiency in Python. Solid understanding of machine learning algorithms, statistics, and model evaluation. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Hands-on experience building and deploying ML models in production. Knowledge of MLOps, CI/CD, model versioning, and ML lifecycle management. Experience with data processing and tools such as Pandas, NumPy, and SQL. Familiarity with cloud platforms such as AWS, Azure, or Google Cloud. Understanding of APIs, Docker, Kubernetes, or other containerization technologies is a plus. Strong problem-solving and analytical skills. Preferred QualificationsExperience with Generative AI, LLMs, NLP, or computer vision. Experience with ML platforms and tools such as MLflow, Kubeflow, or Airflow. Knowledge of distributed computing frameworks such as Spark. Experience with feature engineering and feature stores. Familiarity with model monitoring and observability platforms. Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or a related field. What You’ll DoYou will work on end-to-end machine learning initiatives—from data preparation and experimentation to production deployment and monitoring. You’ll help build reliable ML infrastructure while developing models that deliver measurable business and product impact.Compensation$120,000–$205,000+ annually, depending on experience, technical expertise, location, and overall qualifications.Skills: ml,cloud,machine learning,python