Machine Learning Engineer

Position Overview

Our client is seeking a Machine Learning Engineer to join their team! This position is located in Location: Burbank, California.

Compensation: $70.00 - $100.00 per hour. Please note that the pay range provided is a good faith estimate. Final compensation may vary based on background, knowledge, skills, and location.

Key Responsibilities

• Design, develop, debug, and deploy advanced machine learning applications using frameworks such as PyTorch and TensorFlow
• Build and implement ML models across diverse domains, including computer vision, generative AI, and optimization
• Own the full ML lifecycle, from data preparation and training to deployment, monitoring, and performance tuning
• Evaluate and benchmark academic research, ML tools, and emerging applications to identify innovative approaches
• Stay at the forefront of ML research, assessing new trends and translating them into solutions that support company needs
• Share knowledge internally and contribute to external publications when appropriate
• Facilitate technology transfer between company, external research groups, and partners
• Collaborate closely with creative and technical teams within the company to understand challenges and deliver tailored ML solutions
• Communicate complex ML concepts clearly to both technical and non-technical stakeholders
• Maintain high engineering standards through testing, documentation, and version control
• Optimize ML models for production environments, ensuring scalability and efficiency
• Contribute to internal ML infrastructure and tooling, improving workflows for future projects

Required Qualifications

• Education: Master’s degree in Computer Science or related field
• Experience Required: 7+ years of experience as a Machine Learning Engineer with proven tenure
• 2+ years with experience in PyTorch; TensorFlow a plus
• Strong software engineering skills in Python
• Deep understanding of ML fundamentals and experience building/deploying ML models from scratch in production
• Hands-on experience with large-scale, distributed systems and deploying ML in production environments
• Familiarity with ML Ops tools and practices, Git, Unix/Linux, Docker, and cloud platforms
• Background in applied ML areas such as computer vision, generative AI, graphics, or simulation
• Strong communication skills with the ability to explain technical concepts to varied audiences

Preferred Qualifications

• Prior experience in entertainment, media, or creative industries is a plus

Benefits & Perks

• Benefits: Medical, Dental, & Vision Insurance Plans
• Benefits: Employee-Owned Profit Sharing (ESOP)
• Benefits: 401K offered