Machine Learning Engineer II (AI)

TL;DR

Machine Learning Engineer II (AI): Developing and implementing ML strategies and agentic solutions to enhance workflows and content generation with an accent on real-time systems and end-to-end product delivery. Focus on building ML pipelines, fine-tuning models, and deploying agentic swarms to unlock new creative possibilities.

Location: Hybrid in Los Angeles, United States

Salary: $100,000 – $150,000 a year

Company

An experience-focused technology company blending games, films, and digital worlds to create immersive physical and digital experiences.

What you will do

• Develop and implement a machine learning strategy to enhance internal workflows and content generation.
• Build and operate ML pipelines for data preparation, tuning, training, and deployment optimized for targeted inference.
• Define, deploy, and operate agentic solutions, including integrations with MCP servers and swarms of agents.
• Prototype and test ML-powered features in collaboration with product, engineering, and creative teams.
• Create agent-friendly interfaces and internal tools to streamline production, art, animation, or audio pipelines.
• Implement cutting-edge models and pipelines derived from academic publications.

Requirements

• 4+ years of full-time experience in Machine Learning, Data Engineering, or a related quantitative field.
• Proven track record of delivering end-to-end ML products.
• Experience building training sets, fine-tuning models, and operating ML pipelines.
• Experience building and operating agentic systems.
• Professional experience working on live-service platforms or applications.
• Strong communication skills and ability to provide clear technical direction.

Nice to have

• Experience with near real-time inference systems in games or interactive applications.
• Familiarity with Generative AI (text, image, audio, or animation models).
• Background in game development or creative tooling.
• Experience optimizing models for constrained hardware (mobile, console).
• Knowledge of MLOps best practices and CI/CD for ML systems.

Culture & Benefits

• Generous health care, paid time off, and other corporate benefits.
• Investment in professional growth through ongoing education and mentorship guilds.
• Collaborative and diverse workplace that encourages risk-taking and learning from failure.
• Culture of curiosity, empathy, respect, honesty, and courage.