MLOps Engineer (JAX, PyTorch, Pallas/Triton)

Join a leading AI lab's cutting-edge GenAI team to be at the core of the AI revolution, where your expertise fuels the development of the most advanced Large Language Models.

Join a leading AI lab's cutting-edge GenAI team and help build foundational AI models from the ground up. We're seeking talented MLOps Engineers with deep, hands-on expertise in modern ML frameworks — specifically JAX, PyTorch, and kernel-level programming (Pallas/Triton) — to bring technical excellence and elevate the quality of our AI training data.

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About Cincinnatus LLC

Equal Employment Opportunity

Join a leading AI lab's cutting-edge GenAI team to be at the core of the AI revolution, where your expertise fuels the development of the most advanced Large Language Models. Join a leading AI lab's cutting-edge GenAI team and help build foundational AI models from the ground up. We're seeking talented MLOps Engineers with deep, hands-on expertise in modern ML frameworks — specifically JAX, PyTorch, and kernel-level programming (Pallas/Triton) — to bring technical excellence and elevate the quality of our AI training data. This is a W-2 employment position with Cincinnatus LLC (or appropriate international entity), with the opportunity to be placed at a leading AI Lab as part of their extended workforce. Guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics. Design challenging, domain-relevant tasks across multiple specializations, and write accurate and well-structured solutions to MLOps and ML systems problems. Evaluate MLOps tasks and solutions and provide clear, written technical feedback. Develop guidelines and detailed rubrics/evaluation frameworks to assess training pipeline design, distributed systems reasoning, and kernel-level optimization across tasks. Collaborate with other subject matter experts to ensure consistency and accuracy in training data. 5+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a recognized, top-tier organization. Hands-on production experience with JAX and/or PyTorch at scale — including distributed training strategies (FSDP, tensor parallelism, pipeline parallelism), memory optimization, and framework-level debugging. Experience writing or optimizing custom GPU kernels using Pallas (JAX) or Triton — including tiling strategies, memory layout design, and kernel fusion. Demonstrable career progression. Ability to engage reliably for at least 30 hours/week during weekdays. Strong written communication skills and the ability to explain complex technical decisions clearly. About Cincinnatus LLC: Cincinnatus LLC is an enterprise staffing company that partners with leading technology companies to source and employ highly skilled professionals for contingent and contract-based opportunities. Cincinnatus serves as the employer of record for these engagements, providing W-2 employment, payroll, benefits, and compliance, while placing employees directly within client teams to work on high-impact initiatives. Equal Employment Opportunity: Cincinnatus is proud to be an Equal Employment Opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or any other legally protected characteristic. We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

• Guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics.
• Design challenging, domain-relevant tasks across multiple specializations, and write accurate and well-structured solutions to MLOps and ML systems problems.
• Evaluate MLOps tasks and solutions and provide clear, written technical feedback.
• Develop guidelines and detailed rubrics/evaluation frameworks to assess training pipeline design, distributed systems reasoning, and kernel-level optimization across tasks.
• Collaborate with other subject matter experts to ensure consistency and accuracy in training data.
• 5+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a recognized, top-tier organization.
• Hands-on production experience with JAX and/or PyTorch at scale — including distributed training strategies (FSDP, tensor parallelism, pipeline parallelism), memory optimization, and framework-level debugging.
• Experience writing or optimizing custom GPU kernels using Pallas (JAX) or Triton — including tiling strategies, memory layout design, and kernel fusion.
• Demonstrable career progression.
• Ability to engage reliably for at least 30 hours/week during weekdays.
• Strong written communication skills and the ability to explain complex technical decisions clearly.