Company DescriptionJoin a multibillion-dollar global company that brings together amazing technology, people, and operational scale to become a powerhouse in the memory industry. Headquartered in Rancho Cordova, California, Solidigm combines elements of an established, successful technology company with the spirit, agility, and entrepreneurial mindset of a start-up. In addition to the U.S. headquarters and other facilities in the U.S., the company has international presence in Asia, Europe, and the Americas. Solidigm will continue to lead the world in innovating new Memory technologies with aspirations to be the #1 NAND memory company in the world. At Solidigm, we view problems as opportunities to define innovative solutions that hold the power to change the world and unleash the potential technological needs that the future holds. At Solidigm, we are One Team that fosters a diverse, equitable, and inclusive culture that embraces individual uniqueness and empowers us to bring our best selves to deliver excellence in support of Solidigm's vision and mission to be the go-to partner for optimized data storage solutions. You can be part of the takeoff of an innovative business that develops cutting-edge products, delivers strong business value for customers, provides an engaging workplace for its employees, and serves a greater impact on the world. This is a golden opportunity for the right applicant to join us and help design, build, and lead Solidigm. We want a diverse team of dedicated professionals who will not just be Solidigm team members but contribute to how we shape the future of the organization. We are seeking applicants who will grow and thrive in our culture; be customer inspired, trusting, innovative, team-oriented, inclusive, results driven, collaborative, passionate, and flexible. Job DescriptionThe ML Engineer will be responsible for designing, building, and deploying machine learning models to solve complex business problems. This role involves creating a scalable and efficient environment for data scientists to develop and experiment with models. The ML Engineer will work closely with data scientists to understand their needs, optimize model performance, and ensure seamless integration with existing systems. The ideal candidate will have a strong background in machine learning, software engineering, and cloud-based infrastructure, with a focus on scalability, performance, and reliability. This position requires excellent problem-solving skills, a collaborative mindset, and the ability to stay updated with the latest advancements in machine learning technologies. Key Responsibilities:Create, train, and deploy machine learning models to address complex business challenges. Develop and maintain scalable, efficient, and robust environments for data scientists to experiment and develop models. Work closely with data scientists to understand their requirements, provide technical support, and optimize model performance. Ensure seamless integration of machine learning models with existing systems and workflows. Optimize Model Performance: Continuously monitor and improve the performance, accuracy, and efficiency of machine learning models. Apply best practices in software engineering, including version control, testing, and documentation, to machine learning projects. Keep abreast of the latest advancements in machine learning technologies and methodologies, and apply them to improve existing solutions. Implement and maintain data security and privacy measures in all machine learning projects. Develop and maintain automated pipelines for data preprocessing, model training, and deployment. Provide guidance and training to junior team members and data scientists on machine learning best practices and tools. Qualifications:3+ years experience in data engineering or machine learning functionsBachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field. Proven experience in designing, building, and deploying machine learning models in a production environment. Proficiency in Python. Strong understanding of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Experience with cloud platforms (e.g., AWS, Azure, Google Cloud). Knowledge of software engineering best practices, including version control (e.g., Git), testing, and documentation. Strong analytical and problem-solving abilities to tackle complex business challenges using machine learning. Excellent communication and teamwork skills to work effectively with data scientists, engineers, and other stakeholders. Experience in developing and maintaining automated pipelines for data preprocessing, model training, and deployment.