Senior Machine Learning Workflow Engineer

Job Description

Position Overview

• Location Type: [Not specified]
• Employment Type: Regular
• Salary: [Not specified]

This role is key to harnessing advanced innovations to transform our creative production pipeline and elevate the art of visual storytelling. Reporting to the Director of Creative Technology & Innovation, the ideal candidate will prototype, integrate, develop, and support robust machine learning solutions, aligning innovation with evolving business and creative objectives.

Requirements

• Experience: 10+ years of programming experience, with substantial experience integrating machine learning solutions into creative production pipelines.
• Programming Skills: Extensive Python programming skills, balancing rapid prototyping with robust, production-quality coding practices.
• Machine Learning Frameworks: Strong proficiency in PyTorch, along with working knowledge of key production tools and their APIs.
• Machine Learning Lifecycle: Proven experience managing the complete machine learning lifecycle from initial model training and debugging through to production-ready deployment.
• Communication Skills: Outstanding communication skills, adept at articulating complex technical concepts clearly and effectively to technical artists, production teams, and non-technical stakeholders.
• Adaptability: Ability to thrive in both independent and collaborative environments.

Responsibilities

• Integrate and Optimize ML Models: 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 ML Frameworks: Develop, implement, and maintain flexible, intuitive frameworks that streamline the integration of ML-driven technologies into existing and emerging production workflows.
• Manage Datasets: Leverage existing datasets effectively and provide guidance to future data capture initiatives, contributing to improved decision-making processes and enhancing creative outputs.
• Prototype ML Applications: Prototype and deliver clear, effective proofs-of-concept for machine learning applications, clearly communicating and demonstrating their practical capabilities and constraints.
• Maintain ML Lifecycle: Oversee the full machine learning lifecycle, including model training, debugging training issues, iterative refinement, and deployment.
• Evaluate Open-Source Solutions: Evaluate and recommend open-source solutions compatible with our operational needs and objectives, ensuring technical choices align with production demands.
• Communicate with Stakeholders: 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.
• Champion Best Practices: Communicate complex technical concepts with clarity to diverse stakeholders, ensuring alignment on goals, progress tracking, and collaborative resolution of challenges.
• Promote Continuous Improvement: 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.
• Stay Current with Technology: 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.

Application Instructions

[Not specified]