Machine Learning Ops Engineer

Role Overview

Great Value Hiring is looking for a Machine Learning Ops (MLOps) Engineer with hands-on expertise in JAX, PyTorch, and kernel-level optimization using Pallas/Triton. The role focuses on improving AI training quality and elevating performance of ML training data and MLOps infrastructure.

Responsibilities

• Guide research and engineering teams to close knowledge gaps in MLOps, training infrastructure, and ML framework-level topics
• Design domain-relevant tasks and produce accurate solutions for MLOps/ML systems problems
• Evaluate MLOps tasks/solutions and provide clear written technical feedback
• Create guidelines, rubrics, and evaluation frameworks for:
  • training pipeline design
  • distributed systems reasoning
  • kernel-level optimization
• Collaborate with subject matter experts to ensure consistency and accuracy in training data
• Engage reliably 30+ hours/week on weekdays

Requirements

• 5+ years of professional experience in ML infrastructure, MLOps, or ML systems engineering
• Hands-on production experience with JAX and/or PyTorch at scale, including distributed training approaches such as:
  • FSDP
  • tensor parallelism
  • pipeline parallelism
• Experience with memory optimization and framework-level debugging
• Ability to write or optimize custom GPU kernels using Pallas (JAX) or Triton, including:
  • tiling strategies
  • memory layout design
  • kernel fusion
• Strong written communication skills to explain complex technical decisions clearly

Nice-to-haves

• Demonstrable career progression

Contract / Work Type

• W-2 employment position
• Contingent role