Senior Manager, Machine Learning Engineering

August 22

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Description

• Lead, mentor, and manage a team of machine learning engineers, providing guidance on best practices in ML Ops, infrastructure, and software engineering. • Lead development of infrastructure for ML model training, testing, and deployment. • Be hands-on in the design, development, and deployment of machine learning models and systems, ensuring they meet high standards of performance, scalability, and reliability. • Collaborate with data scientists, product managers, and other stakeholders to define project requirements and deliverables. • Develop and maintain ML Ops pipelines, ensuring efficient model training, deployment, and monitoring. • Implement and manage infrastructure for large-scale data processing, model training, and inference. • Drive continuous improvement in engineering practices, including code quality, testing, and deployment automation. • Stay up-to-date with the latest trends and advancements in machine learning, software engineering, and cloud infrastructure to inform team strategy and direction. • Manage project timelines, resources, and deliverables, ensuring projects are completed on time and within budget. • Foster a culture of innovation, collaboration, and continuous learning within the engineering team.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field. • 8+ years of experience in software engineering, with a focus on machine learning, ML Ops, and infrastructure. • Minimum of 3 years of experience in a leadership or management role, with a proven track record of leading engineering teams to successful project outcomes. • Strong understanding of machine learning frameworks, tools, and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). • Experience with ML Ops practices, including model versioning, continuous integration, and automated deployment. • Proficiency in software engineering practices, including object-oriented design, code versioning, and testing. • Experience with cloud platforms (e.g., AWS, Google Cloud, Azure) and distributed computing. • Strong problem-solving skills, with the ability to lead teams in troubleshooting complex technical challenges. • Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams. • Demonstrated ability to manage multiple projects simultaneously, prioritizing tasks and managing resources effectively.