MLOps Engineer (JAX, PyTorch, Pallas/Triton)

Summary

Weekday AI is a cutting-edge AI research initiative focused on Generative AI and the development of next-generation Large Language Models. They are seeking experienced MLOps Engineers to leverage their expertise in modern machine learning frameworks and large-scale training infrastructure to enhance AI systems and collaborate with research and engineering teams.

Responsibilities

• Partner with research and engineering teams to strengthen AI model capabilities in MLOps, ML infrastructure, and large-scale training systems
• Design challenging, real-world MLOps and machine learning systems tasks that reflect production engineering scenarios
• Develop accurate, well-documented solutions to complex ML infrastructure and training pipeline problems
• Review and evaluate technical tasks and AI-generated solutions, providing clear and actionable written feedback
• Create detailed evaluation rubrics and scoring frameworks for topics including:
• Distributed training architectures
• ML pipeline design
• Infrastructure optimization
• Kernel-level programming
• Performance tuning
• Collaborate with fellow subject matter experts to maintain consistency, quality, and technical accuracy across training datasets
• Contribute domain expertise to improve the reasoning capabilities of advanced AI systems

Skills

• Minimum 2 years of professional experience in MLOps, Machine Learning Infrastructure, or ML Systems Engineering within a recognized technology organization
• Hands-on production experience with JAX and/or PyTorch in large-scale machine learning environments
• Practical experience developing or optimizing custom GPU kernels using Pallas (JAX) or Triton
• Strong understanding of distributed training systems, model optimization, and scalable ML infrastructure
• Demonstrated career growth and increasing technical responsibility
• Availability to work 40 hours per week during standard weekday business hours
• Excellent written communication skills with the ability to clearly explain technical concepts and architectural decisions
• Experience designing and optimizing large-scale ML training pipelines
• Knowledge of distributed computing and GPU performance optimization
• Familiarity with evaluation methodologies for AI models and ML systems
• Experience collaborating with research teams on advanced machine learning projects
• Passion for advancing AI infrastructure and frontier model development

Qualifications

Must Haves

• Minimum 2 years of professional experience in MLOps, Machine Learning Infrastructure, or ML Systems Engineering within a recognized technology organization
• Hands-on production experience with JAX and/or PyTorch in large-scale machine learning environments
• Practical experience developing or optimizing custom GPU kernels using Pallas (JAX) or Triton
• Strong understanding of distributed training systems, model optimization, and scalable ML infrastructure
• Demonstrated career growth and increasing technical responsibility
• Availability to work 40 hours per week during standard weekday business hours
• Excellent written communication skills with the ability to clearly explain technical concepts and architectural decisions

Nice to Haves

• Experience designing and optimizing large-scale ML training pipelines
• Knowledge of distributed computing and GPU performance optimization
• Familiarity with evaluation methodologies for AI models and ML systems
• Experience collaborating with research teams on advanced machine learning projects
• Passion for advancing AI infrastructure and frontier model development

Benefits

• Fully remote engagement
• Independent contractor basis
• 40-hour-per-week remote engagement
• Payments are processed weekly through Stripe or Wise based on approved work completed