Senior Machine Learning Workflow Engineer

Senior Machine Learning Workflow Engineer

Requirements

• 10+ years of programming experience, with substantial experience integrating machine learning solutions into creative production pipelines.
• Extensive Python programming skills, balancing rapid prototyping with robust, production-quality coding practices.
• Strong proficiency in PyTorch, along with working knowledge of key production tools and their APIs.
• Proven experience managing the complete machine learning lifecycle from initial model training and debugging through to production-ready deployment.
• Outstanding communication skills, adept at articulating complex technical concepts clearly and effectively to technical artists, production teams, and non-technical stakeholders.
• Ability to thrive in both independent and collaborative environments, proactively identifying process improvements and contributing positively to team efficiency.
• Active engagement with current machine learning advancements, coupled with the ability to swiftly adapt and integrate new techniques and technologies.
• High adaptability, exceptional problem-solving abilities, and comfort navigating the uncertainties and pace of a dynamic creative production setting.

Responsibilities

• Integrate and optimize machine learning models within established 2D and 3D software environments, ensuring seamless workflows in DCCs while providing ongoing support for systems already deployed in production.
• Develop, implement, and maintain flexible, intuitive frameworks that streamline the integration of ML-driven technologies into existing and emerging production workflows.
• Leverage existing datasets effectively and provide guidance to future data capture initiatives, contributing to improved decision-making processes and enhancing creative outputs.
• Prototype and deliver clear, effective proofs-of-concept for machine learning applications, clearly communicating and demonstrating their practical capabilities and constraints.
• Oversee the full machine learning lifecycle, including model training, debugging training issues, iterative refinement, and deployment.
• Evaluate and recommend open-source solutions compatible with our operational needs and objectives, ensuring technical choices align with production demands.
• Maintain clear and frequent communication with research scientists, technical artists, and production teams to bridge the gap between advanced research and practical, efficient production use.
• Communicate complex technical concepts with clarity to diverse stakeholders, ensuring alignment on goals, progress tracking, and collaborative resolution of challenges.
• Champion best practices and continuous improvements in our machine learning and software development methodologies, focusing on robust coding standards, effective package management, and efficient build processes.
• Adapt proactively to a fast-paced, rapidly evolving technology landscape, maintaining a keen interest and engagement with the latest machine learning research, developments, and methodologies.

Preferred Qualifications

No preferred qualifications provided.