Remote Senior Machine Learning Engineer

Summary

Join Wave as a Senior Machine Learning Engineer and help shape the future of AI capabilities by combining traditional machine learning with cutting-edge Generative AI technologies. You'll develop, scale, and maintain production-grade ML models while applying innovative GenAI approaches to solve customer and business challenges.

Requirements

• 5+ years of experience in building and deploying production-grade machine learning systems
• Strong Python and SQL skills, with familiarity in containerization tools like Docker, and an understanding of secure model access via APIs
• Familiarity with AWS cloud services, including SageMaker, Amazon Bedrock, and Amazon Q
• Experience automating MLOps pipelines and deploying models for both batch and real-time inference
• Hands-on expertise in developing GenAI proofs-of-concept and taking them through to production, as well as experience scaling traditional ML models for production at scale
• A solid understanding of AI explainability, including experience with model interpretability techniques, observability, and monitoring to ensure performance and reliability
• Familiarity with applying FinOps concepts to optimize cloud resources, reduce costs, and ensure the efficient scaling of AI models
• A collaborative mindset, with strong communication skills and a passion for mentoring and growing the capabilities of the ML team

Responsibilities

• Collaborate with product and technical teams to develop and deploy both traditional ML models and GenAI systems
• Drive the automation and scalability of Wave’s MLOps stack, supporting the scaling of production-ready ML models
• Design and build GenAI proofs-of-concept (PoCs), evaluating and deploying those that meet performance and business impact criteria into production
• Contribute to the development of a unified API to provide secure, scalable, and reliable access to AI models
• Monitor the reliability of all deployed models, enhancing their observability and explainability to meet ethical and regulatory standards
• Mentor other engineers, sharing best practices for model development, deployment, and monitoring in both traditional ML and GenAI contexts

Benefits

• Work From Where You Work Best: flexible work arrangements
• We Care About Future You: diverse learning experiences, educational allowances, mentorship, and more
• We Support the Full You: investment in health & wellness
• We Take Care of the Fundamentals: fair compensation, office perks, and other benefits