Machine Learning Engineer

Responsibilities

• Design, build, and operate scalable ML infrastructure on AWS, including compute, storage, and networking
• Develop and maintain MLOps workflows for data versioning and experimentation
• Build and manage distributed systems for large-scale data processing and model evaluation
• Own architecture decisions for ML infrastructure to drive reliability, scalability, and cost efficiency
• Implement system observability through monitoring, logging, and alerting
• Integrate UIs like OpenWebUI or Gradio for data quality assurance
• Partner with researchers to translate experimental workflows into robust, production-ready systems

Requirements

• 3+ years of experience in MLOps, ML infrastructure, or related platform engineering roles
• Strong experience with AWS and core cloud services
• Experience designing and operating distributed systems using Kubernetes, Ray, or similar frameworks
• Proficiency in Python, Docker, and Git
• Familiarity with data pipeline orchestration tools such as Spark or Kafka
• Experience implementing observability practices including monitoring and alerting

Preferred Qualifications

• Experience in fast-paced or research-driven environments
• Experience with large-scale video or multimodal data pipelines
• Experience building automated model evaluation or benchmarking systems
• Knowledge of cost optimization and security in multi-tenant environments
• Familiarity with modern AI-assisted coding tools

Benefits

• Comprehensive medical, dental, and vision benefits
• Annual bonus and 401K plan
• Generous paid time off, sick leave, and holidays
• Paid parental leave
• Employee Assistance Program
• Life insurance and disability coverage

About the Company

The Institute of Foundation Models is a dedicated research lab focused on building, understanding, and managing the risks of foundation models. Our AllWorld Team is pioneering the development of PAN (Physical, Agentic, and Networked) world models to unlock machine intelligence beyond language.