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Ai Model Training Jobs (NOW HIRING)

Unconventional AI

AI Systems, Training

Palo Alto, CA · On-site

$123K - $168K/yr

They are seeking a key contributor to build a next-generation ML model training platform and co-design training systems alongside novel AI models and hardware. Responsibilities : • Build and ...

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How much do ai model training jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for ai model training in the United States is $31.37, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $39.18 per hour, depending on experience, location, and employer.

What is an AI Model Training job?

An AI Model Training job involves preparing, training, and optimizing machine learning models using data. Professionals in this role preprocess datasets, select appropriate algorithms, adjust model parameters, and evaluate performance to improve accuracy. They work with frameworks like TensorFlow or PyTorch and may fine-tune models for specific tasks such as image recognition or natural language processing. This job requires expertise in data science, programming, and statistical analysis to ensure models perform efficiently in real-world applications.

What are the key skills and qualifications needed to thrive in the Ai Model Training position, and why are they important?

To excel in AI Model Training, you need a strong background in machine learning, programming (especially Python), data analysis, and a relevant degree such as computer science or engineering. Familiarity with frameworks like TensorFlow or PyTorch, experience with cloud computing platforms, and certifications in AI or data science are highly advantageous. Strong problem-solving skills, attention to detail, and the ability to communicate complex ideas effectively make candidates stand out. These competencies are crucial for developing accurate, efficient AI models and collaborating seamlessly within multidisciplinary teams.

What are the typical work responsibilities of someone in AI Model Training?

Professionals in AI Model Training are typically responsible for collecting, preparing, and processing large datasets, designing and implementing machine learning models, and evaluating their performance using statistical methods. You may work closely with data engineers, software developers, and product managers to ensure models meet business objectives and integrate smoothly into existing systems. Regular responsibilities also include tuning hyperparameters, troubleshooting model issues, and staying up-to-date with the latest advancements in AI. This role often involves a mix of independent technical work and collaborative problem-solving sessions with the broader team.
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Ai Model Training jobs near you
Infographic showing various Ai Model Training job openings in the United States as of May 2026, with employment types broken down into 10% Internship, 70% Full Time, and 20% Contract. Highlights an 70% In-person, and 30% Remote job distribution, with an average salary of $65,246 per year, or $31.4 per hour.

Engineering Manager (AI Research & Model Training)

Perplexity

San Francisco, CA • On-site

$300K - $485K/yr

Full-time

Posted yesterday

Job description

Perplexity is seeking a Research Engineering Manager to lead the team of all-star AI researchers and engineers responsible for developing the models that drive our products. Our team has developed some of the most advanced models for agentic research, query understanding, and other domains that require accuracy and depth. As we expand our userbase and portfolio of product surfaces, our in-house models are increasingly critical to providing a premium, high-taste experience for the world's most sophisticated users.
You will dive into our rich datasets of conversational and agentic queries, leveraging cutting‑edge training techniques to scale AI model performance. Through hands-on technical and organizational leadership, you will empower your team to develop SotA models for the use cases that matter most to our business and our users.
Responsibilities
  • Lead a team of researchers and engineers focused on training SotA models for Perplexity-relevant use cases, leveraging the latest supervised and reinforcement learning techniques.
  • Drive research and engineering efforts to develop production models through advanced model training and alignment techniques, including RL, SFT, and other approaches.
  • Become deeply familiar with the team's technical stack, leading from the front through hands-on technical contributions.
  • Own the data, training, and eval pipelines required to train and continuously improve LLM models.
  • Design and iterate on model training and finetuning algorithms (e.g., preference‑based methods, reinforcement learning from human or AI feedback) through an approach that balances scientific rigor and iteration velocity.
  • Design evaluations and improve the production model training pipeline to reliably deliver models that lie on the Pareto frontier of speed and quality.
  • Work closely with engineering teams to integrate in-house models into our product and rapidly iterate based on real‑world usage.
  • Manage day‑to‑day execution, project planning, and prioritization for the model training team to hit ambitious quality and performance goals.
Qualifications
  • Proven experience with large-scale LLMs and Deep Learning systems.
  • Strong Python and PyTorch skills; versatility across languages and frameworks is a plus.
  • Experience leading or managing research or engineering teams working on large-scale AI model development, including driving complex projects from idea to production.
  • Self‑starter with a willingness to take ownership of tasks and navigate ambiguity in a fast‑moving environment.
  • Passion for tackling challenging problems in AI model quality, speed, safety, and reliability.
  • 10+ years of technical experience, with at least 2 of those years as a manager and at least 4 of those years working on large-scale AI model development.
Nice-to-have
  • PhD in Machine Learning or related areas.
  • Experience training very large Transformer-based models with techniques such as SFT, DPO, GRPO, RLHF‑style methods, or related preference‑based optimization approaches.
  • Prior experience designing evaluations and production training pipelines for large‑scale models in a high‑growth environment.

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