MLOps Engineer

$35/hr

25 people hired

RemoteGlobalTechnology3-5 YearsContractPosted Aug 11, 2026

About this role

MLOps Engineer responsible for supporting frontier AI model training and evaluation through advanced ML infrastructure, distributed training, and framework-level optimization. The role involves working extensively with JAX, PyTorch, and GPU kernel programming using Pallas or Triton, while designing challenging MLOps tasks, developing technically rigorous solutions, and evaluating AI-generated outputs. Engineers will contribute expertise in training infrastructure, ML systems, distributed computing, and performance optimization to improve next-generation AI models.

MLOps & ML Infrastructure, JAX & PyTorch Engineering, GPU Kernel Optimization

Responsibilities

Design advanced MLOps and ML systems evaluation tasks.
Develop accurate solutions for training infrastructure and ML framework problems.
Evaluate AI-generated MLOps solutions and provide technical feedback.
Work with JAX and PyTorch for large-scale model training workflows.
Develop and optimize custom GPU kernels using Pallas or Triton.
Create evaluation rubrics for training pipelines, distributed systems, and kernel optimization.
Identify knowledge gaps and improve AI model performance on ML infrastructure topics.
Collaborate with researchers and technical SMEs to maintain high-quality AI training data.

Qualifications

2+ years of professional experience in MLOps, ML infrastructure, or ML systems engineering at a recognized organization.
Hands-on production experience with JAX and/or PyTorch at scale.
Experience developing or optimizing custom GPU kernels with Pallas or Triton.
Strong understanding of ML training infrastructure, distributed systems, and performance optimization.
Demonstrable career progression in ML systems or infrastructure engineering.
Strong technical writing and ability to clearly explain complex engineering decisions.
Must be US-based and available 40 hours per week on weekdays with no conflicting engagements.