Remote Director, Machine Learning Engineering

Summary

Join Xometry as Machine Learning Engineering Director and lead a team of engineers, manage infrastructure for ML model training, testing, and deployment, and contribute to the design, development, and deployment of machine learning models and systems.

Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or a related field
• 10+ 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
• 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
• Excellent communication and interpersonal skills
• Demonstrated ability to manage multiple projects simultaneously, prioritizing tasks and managing resources effectively

Responsibilities

• Lead, mentor, and manage a team of machine learning engineers
• 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
• Collaborate with data scientists, product managers, and other stakeholders to define project requirements and deliverables
• Develop and maintain ML Ops pipelines
• Implement and manage infrastructure for large-scale data processing, model training, and inference
• Drive continuous improvement in engineering practices
• Manage project timelines, resources, and deliverables
• Foster a culture of innovation, collaboration, and continuous learning within the engineering team

Preferred Qualifications

• Experience with containerization technologies (e.g., Docker, Kubernetes)
• Knowledge of big data technologies (e.g., Hadoop, Spark) and data engineering practices
• Experience with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI)