Machine Learning Engineer II

Magnopus•Los Angeles, CA

About The Position

Magnopus is looking for a Machine Learning Engineer who thrives at the intersection of product innovation, real-time systems, and creative collaboration. In this role, you won’t just build models, you’ll help define how machine learning transforms our projects and unlocks entirely new experiences. You’ll partner closely with product, project, engineering, and creative teams to identify high-impact opportunities for ML, rapidly prototype solutions, and bring them into production at scale. This is a hands-on role where strategic thinking meets deep technical execution.

Requirements

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

Nice To Haves

Experience with near real-time inference systems in games or interactive applications
Familiarity with generative AI (e.g., text, image, audio, or animation models)
Background in game development or creative tooling
Experience optimizing models for performance on constrained hardware (mobile, console, etc.)
Knowledge of MLOps best practices, including CI/CD for ML systems
Experience implementing solutions contemplated in academic publications

Responsibilities

Work with teams and projects to develop and implement a machine learning (ML) strategy to enhance our workflows and content generation capabilities.
Educate the team on what is possible with ML.
Build pipelines and prepare data for tuning, training, and deployment of models optimized for targeted inference.
Partner with teams to prototype and test ML-powered features.
Develop internal tools using ML to streamline workflows for production, art, animation, or audio pipelines.
Define, deploy, and operate agentic solutions, including integrations with MCP servers, tool definitions, and swarms of agents.
Create agent friendly interfaces for our tools and processes.
Implement models and pipelines from academic publications.
Remain current with ML and real-time AI trends and ensure performance scalability across platforms.