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Pytorch Jobs Near Me

SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment ...

Gen AI Developer Intern

Dublin, OH ยท On-site

$18.50 - $24.50/hr

... PyTorch, Flask, Django, AWS Sagemaker, AWS Bedrock and Azure - ML Studio, cognitive services & OpenAI. โ€ข Understanding of ethical considerations and biases in AI systems. Skills:

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Pytorch information

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$144.3K

$207K

How much do pytorch jobs pay per year?

As of Sep 12, 2026, the average yearly pay for pytorch in the United States is $144,320.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,000.00 and $176,500.00 per year, depending on experience, location, and employer.

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Cities with the most Pytorch job openings:

What states have the most Pytorch jobs?

States with the most job openings for Pytorch jobs include:

What are the most commonly searched types of Pytorch jobs?

The most popular types of Pytorch jobs are:

A map of the United States highlighting the number of Pytorch job openings by state according to ZipRecruiter. The image is accompanied by a detailed chart listing the number of Pytorch job openings in each state, with California having the most at 2 and Hawaii the least at 0.

Machine Learning Engineer

Columbus, OH โ€ข On-site

Full-time

Re-posted 27 days ago


Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems