Machine Learning Engineer - AI Platform

About the position Docusign is seeking a passionate and talented Machine Learning Engineer to join our AI Infrastructure team. This role is pivotal in building Docusign's centralized platform for creating, managing, and deploying advanced AI/ML solutions, aimed at enhancing customer journeys throughout the Docusign Agreement Cloud. As a Machine Learning Engineer, you will be involved in all aspects of the machine learning lifecycle, which includes the research platform, training and deployment pipelines, as well as labeling and serving infrastructure. You will collaborate with a team of expert applied researchers specializing in various domains such as document understanding, natural language processing (NLP), and computer vision, to prototype and productionize solutions for real business use cases at scale. In this individual contributor role, you will report to the Senior Manager of Machine Learning. Your responsibilities will include collaborating with Applied Science, Product, and other engineers across multiple offices and time zones to create and deliver new machine learning products and features on time. You will contribute to the development of AI infrastructure by building highly scalable gRPC-based services to enable both offline and online machine learning pipelines, ensuring enterprise-grade security and reliability. Additionally, you will develop systems to optimize and create AI-assisted data labeling processes while maintaining appropriate data governance and security standards. Your role will also involve optimizing model performance in production, improving platform observability, and implementing tools to monitor and analyze the performance of deployed AI services. Responsibilities • Collaborate with Applied Science, Product and other engineers across multiple offices and time zones to create and deliver new machine learning products and features on time , • Contribute to the development of AI infrastructure by building highly scalable gRPC-based services to enable offline and online machine learning pipelines, ensuring enterprise-grade security and reliability , • Develop systems to optimize and create AI-assisted data labeling processes while maintaining appropriate and thorough data governance and security standards , • Contribute to the development of scalable model training infrastructure to minimize the time it takes to deploy the candidate models to production , • Optimize model performance (including open source and licensed LLMs) in production leveraging best practices for CPU/GPU inference on NVIDIA Triton with TensorRT / ONNX , • Improve platform observability by implementing tools and required infrastructure to monitor and analyze the performance of deployed AI services Requirements • Minimum of 5 years of related experience with a Bachelor's degree; or 3 years of related experience with a Master's degree; or a PhD without experience; or equivalent experience , • Experience building/consuming RESTful and gRPC based web-services , • Experience with CI/CD build pipelines, integrated tests, and test-driven development , • Familiarity with cloud deployment technologies, such as Kubernetes or Docker containers , • Experience with designing and scaling fullstack or distributed backend systems , • Experience with Java, Python or similar programming languages Nice-to-haves • Experience building machine learning products, data pipelines, or machine learning training and deployment systems , • Experience with deployment and monitoring of machine learning models , • Ability and desire to move across technology stacks fluently and easily , • Experience with LLMs , • Familiarity with NLP, Computer Vision domains , • Familiarity with Open AI APIs and libraries Benefits • Paid Time Off: earned time off, as well as paid company holidays based on region , • Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement , • Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment , • Retirement Plans: select retirement and pension programs with potential for employer contributions , • Learning and Development: options for coaching, online courses and education reimbursements , • Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events Apply Job!