MLOps Engineer (JAX, PyTorch, Pallas/Triton)

Key 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.

Required Qualifications

• 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.

Preferred Skills

• 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

This is a full-time, 40-hour-per-week remote engagement requiring full weekday availability.

Pay

$70-$110 per hour

Schedule

Full-time, 40-hour-per-week remote engagement requiring full weekday availability.