The Machine Learning Engineer is responsible for developing, implementing, and productizing machine learning models that deliver business value. This role focuses on transforming machine learning prototypes into scalable, production-grade solutions. The ML Engineer works closely with data scientists, software engineers, and product teams to ensure that AI/ML models are effectively integrated into products, services, and applications, enabling data-driven decisions, and enhancing customer experiences. 

What You Will Be Doing Productize ML Models: Work alongside data scientists who originate and design machine learning models to take these prototypes from concept to production-ready solutions. Focus on building scalable and efficient models for integration into live environments. End-to-End Model Deployment: Help deploy machine learning models into production environments, ensuring that they function reliably and are easily maintainable. This includes some DevOps practices such as CI/CD pipelines for model deployment. Data Preparation and Feature Engineering: Work with data scientists to preprocess, clean, and transform data to prepare it for production-grade machine learning models. Collaborate with Product Teams: Support product managers and stakeholders in defining model requirements and ensuring alignment with product goals. Model Optimization and Scaling: Assist in optimizing machine learning models to improve performance and scalability for large-scale production environments. Build and Maintain ML Pipelines: Help build and maintain ML pipelines to automate model training, testing, and deployment, incorporating DevOps principles to streamline processes. Monitoring and Maintenance: Monitor deployed models, check for performance drift, and work on necessary model updates, integrating monitoring tools for tracking model health. Model Evaluation and Reporting: Assist in evaluating model performance, analyzing metrics, and suggesting improvements. Documentation and Best Practices: Support documentation efforts related to model deployment processes, performance metrics, and best practices. Stay Current: Learn about emerging trends in AI/ML technologies and apply new methods to improve product offerings. Your Qualifications Experience: 0-2 years of experience in machine learning engineering or a related field. Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field. Technical Expertise: Basic understanding of machine learning algorithms, deployment practices, and integration. Product Focus: Some exposure to integrating machine learning models into products or production systems. Enthusiasm for AI/ML: Passion for AI/ML and a desire to contribute to solving business problems through machine learning. DevOps Exposure: Familiarity with DevOps practices in model deployment, including CI/CD pipelines, version control, and automated testing. A few things we have to offer:  Competitive compensation  Great Healthcare + Dental + Vision Flexible PTO Culture of support, encouraging Life-Work balance 401k match FSA and HSA options Employee Assistance Program Paid Parental Leave Representing a company with 4,000+ clients and a 99% retention rate Accelerated title and salary growth potential  A fun and energetic work environment that makes you excited to go to work every day