MLOps Engineer
US$70 – US$110 / hr base salary
Posted 29 Jul 2026
Advertised as “MLOps Engineer (JAX, PyTorch, Pallas/Triton)”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 2+ yrs
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Part-time
- Visa sponsorship
- Not offered
| Base salary | US$70 – US$110 / hr |
|---|
This role is for one of our clients
Compensation: $70-$110 per hour
Join a cutting-edge AI research initiative at the forefront of Generative AI and contribute to the development of next-generation Large Language Models. We are seeking experienced MLOps Engineers with deep expertise in modern machine learning frameworks, large-scale training infrastructure, and kernel-level optimization.
In this role, you'll leverage your knowledge of JAX, PyTorch, and custom GPU kernel programming (Pallas/Triton) to create, evaluate, and refine high-quality technical tasks that help train frontier AI systems. You'll collaborate with AI researchers and engineering teams to improve model reasoning across MLOps, distributed training, and ML infrastructure topics.
This is a full-time, 40-hour-per-week remote engagement requiring full weekday availability.
Requirements
Key Responsibilities
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Partner with research and engineering teams to strengthen AI model capabilities in MLOps, ML infrastructure, and large-scale training systems.
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Design challenging, real-world MLOps and machine learning systems tasks that reflect production engineering scenarios.
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Develop accurate, well-documented solutions to complex ML infrastructure and training pipeline problems.
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Review and evaluate technical tasks and AI-generated solutions, providing clear and actionable written feedback.
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Create detailed evaluation rubrics and scoring frameworks for topics including:
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Distributed training architectures
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ML pipeline design
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Infrastructure optimization
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Kernel-level programming
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Performance tuning
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Collaborate with fellow subject matter experts to maintain consistency, quality, and technical accuracy across training datasets.
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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 and/or in large-scale machine learning environments.