Machine Learning Engineer Location: St. Louis, Missouri Employment Type: Full-time, Direct Hire Citizenship Requirement: Must be a U.S. Citizen Salary Range: $112,000 – $145,000 About the Role: You’ll design, develop, and deploy machine learning models to solve real-world business challenges. You will collaborate with data engineers, software developers, and product managers to integrate intelligence at scale. This role provides strong career growth in an emerging tech environment. Key Responsibilities: - Develop and train ML models (e.g., regression, classification, recommendation systems) - Feature engineering and data preprocessing using structured/unstructured data - Build and maintain production-quality ML pipelines (e.g., ETL, model serialization, API deployment) - Analyze model performance, tune hyperparameters, and iterate to improve outcomes - Integrate models into software systems or cloud-based platforms - Collaborate with cross-functional teams to align ML deliverables with business objectives - Maintain thorough documentation and stay current with latest ML methodologies Required Qualifications: - Education: Bachelor’s degree (minimum) in Computer Science, Data Science, Information Technology, or related field - Experience: 1–3 years applying ML in professional settings—internships and co-ops count - Citizenship: U.S. Citizen (required) - Technical Proficiency: - Programming in Python (or Java/C++) - Familiarity with ML libraries: scikit-learn, TensorFlow, PyTorch, or XGBoost - Experience with data pipelines/tools: Spark, Airflow, or Pandas - Knowledge of model deployment (e.g., REST APIs, AWS SageMaker, Docker) - Sound understanding of data structures, algorithms, and software engineering fundamentals - Analytical Skills: - The ability to analyze large datasets and extract insights - Strong problem-solving and debugging capabilities - Soft Skills: - Excellent teamwork and written/verbal communication - Self-driven, with a mindset to learn and adapt rapidly - Strong attention to quality, documentation, and best practices Preferred Qualifications: - Master’s degree in a quantitative discipline - Experience with deep learning frameworks (TensorFlow, PyTorch) - Familiarity with MLOps platforms (MLflow, Kubeflow) - Exposure to cloud environments (AWS, GCP, Azure) - Understanding of big-data ecosystems (Hadoop, Kafka) What You’ll Gain: - A competitive base salary ($112–145K) - Opportunity to work on real-world ML systems from end-to-end - Professional development programs, mentorship, and career advancement - Benefits: Health, dental, vision insurance; 401(k); paid time off; and discretionary bonuses/equity How to Apply: Interested? Please submit your resume and a brief cover letter highlighting your hands-on ML experience and relevant projects or achievements.