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Ai Model Jobs in Bothell, WA (NOW HIRING)

Pwc

AI Engineer

Seattle, WA · On-site

$50K - $112K/yr

Your work will involve designing AI systems, data wrangling, and software implementation to enable the AI models to be useful and scalable. As an Associate, you will focus on learning and ...

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Ai Model information

See Bothell, WA salary details

$11

$35

$74

How much do ai model jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for ai model in Bothell, WA is $35.07, according to ZipRecruiter salary data. Most workers in this role earn between $21.25 and $43.80 per hour, depending on experience, location, and employer.

What is an AI model?

AI models are computer programs designed to simulate human intelligence by learning patterns from data and making predictions or decisions based on that learning. These models can perform a variety of tasks, such as recognizing speech, translating languages, analyzing images, and generating text. AI models are created using machine learning algorithms and are trained on large datasets to improve their accuracy and performance. Popular examples include neural networks, decision trees, and support vector machines. The effectiveness of an AI model depends on the quality of the data, the chosen algorithm, and the training process.

What are the key skills and qualifications needed to thrive as an AI model, and why are they important?

To excel as an AI Model Developer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and typically a degree in computer science, data science, or a related field. Familiarity with ML frameworks like TensorFlow or PyTorch, cloud platforms, and relevant certifications such as TensorFlow Developer or AWS Machine Learning Specialty are valuable. Critical thinking, continuous learning, and effective collaboration with interdisciplinary teams are key soft skills for success. These competencies enable the creation of accurate, reliable AI models that can effectively solve complex real-world problems.

What are some common challenges faced by professionals working as AI model developers, and how can they address them?

Professionals working as AI Model developers often encounter challenges such as managing large and complex datasets, ensuring model accuracy, and addressing issues of bias in algorithms. They may also need to balance the trade-off between model performance and interpretability, especially when deploying models in production environments. To overcome these challenges, AI Model developers typically collaborate closely with data engineers, domain experts, and other stakeholders, regularly validate their models, and stay updated with the latest advancements in the field to adopt best practices.

What is the difference between Ai Model vs Data Scientist?

AspectAi ModelData Scientist
Required CredentialsKnowledge of machine learning, programming skills, sometimes certifications in AI/MLDegree in data science, statistics, computer science; certifications beneficial
Work EnvironmentFocus on developing, training, and deploying AI modelsData analysis, interpretation, and visualization; often collaborates with AI teams
Industry UsageUsed in AI development, automation, and predictive modelingApplied across industries for insights, reporting, and decision-making

While both roles involve working with data and algorithms, an Ai Model primarily focuses on creating and refining AI systems, whereas a Data Scientist analyzes data to generate insights and supports AI development. The roles often overlap but serve distinct functions within the data and AI ecosystem.

What is the easiest AI Model job to get into?

Entry-level AI model jobs often include roles such as data annotator or junior machine learning assistant, which typically require basic programming skills in Python and understanding of data labeling. These positions usually have lower experience requirements and may offer on-the-job training, making them accessible for beginners entering the AI field.

What are popular job titles related to Ai Model jobs in Bothell, WA?

For Ai Model jobs in Bothell, WA, the most frequently searched job titles are:

What job categories do people searching Ai Model jobs in Bothell, WA look for?

The top searched job categories for Ai Model jobs in Bothell, WA are:

What cities near Bothell, WA are hiring for Ai Model jobs?

Cities near Bothell, WA with the most Ai Model job openings:

Ai Model jobs near you
Infographic showing various Ai Model job openings in Bothell, WA as of August 2026, with employment types broken down into 2% Internship, 63% Full Time, 18% Part Time, and 17% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $72,937 per year, or $35.1 per hour.

AI Training Infrastructure Engineer

Designworks Talent

Bellevue, WA • On-site

$180 - $240/hr

Other

Medical, Dental, Vision, Retirement

Posted 29 days ago

Job description

AI Training Infrastructure Engineer

Location: Hybrid | Bellevue, WA Area
Titles: Senior and Staff (multiple roles available)

Build the Training Infrastructure Powering Next-Generation AI ModelsAbout the Opportunity

A well-funded, rapidly growing AI infrastructure company is building a next-generation cloud platform designed to power the full lifecycle of artificial intelligence. The organization is developing a comprehensive AI infrastructure, platform, and services portfolio that supports the full spectrum of AI workloads—including large-scale compute, model training, fine-tuning, inference, and emerging agentic AI applications.

Backed by significant long-term investment, the company combines the speed, ownership, and innovation of a startup with the stability and resources of an established parent organization. Engineering teams are intentionally lean, highly collaborative, and AI-native, leveraging modern tooling and automation to build infrastructure capable of supporting the industry\'s most demanding AI workloads.

We\'re seeking AI Training Infrastructure Engineers to build and scale the distributed systems that power large-scale AI model training. This team focuses on reliability, efficiency, and operational excellence across GPU clusters, enabling researchers and engineers to train and deploy advanced AI models at scale.

The Opportunity

This is a foundational engineering role focused on building the infrastructure layer behind large-scale AI training workloads. You\'ll work on distributed training systems, GPU clusters, model pipelines, and the tooling required to make AI development more reliable, efficient, and scalable.

You\'ll collaborate closely with infrastructure, orchestration, performance, and machine learning teams to solve complex challenges around distributed computing, fault tolerance, training efficiency, and production readiness.

This opportunity is ideal for engineers who enjoy building highly scalable systems and working at the intersection of AI research, infrastructure engineering, and distributed computing.

What You\'ll Do
  • Build and scale distributed training infrastructure supporting large AI models across large GPU clusters.
  • Design and improve systems that increase training reliability, efficiency, and resource utilization.
  • Develop solutions for fault tolerance, checkpointing, recovery, and large-scale training operations.
  • Integrate AI models into production training pipelines in partnership with platform, orchestration, and performance engineering teams.
  • Diagnose and resolve issues impacting training throughput, stability, reliability, and cost efficiency.
  • Build tools and automation that improve the developer experience for AI researchers and engineers.
  • Establish best practices for training infrastructure, operational processes, and platform reliability.
  • Contribute to the evolution of the AI infrastructure platform as an early member of the engineering team.
What We\'re Looking For
  • Hands-on experience building and operating distributed training systems or large-scale machine learning infrastructure.
  • Experience supporting large AI models, foundation models, post-training workflows, or similar ML systems.
  • Strong understanding of the reliability, scalability, and efficiency challenges associated with multi-node GPU training.
  • Experience integrating training systems with production machine learning pipelines.
  • Strong programming skills and experience working with complex distributed systems.
  • Ability to independently own technically challenging projects in a fast-moving engineering environment.
  • Comfortable operating with high ownership and limited process overhead.
Preferred Qualifications
  • Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, Megatron-LM, Ray, or similar technologies.
  • Experience with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), or other post-training workflows.
  • Background operating AI training infrastructure at scale within a hyperscaler, AI research organization, cloud provider, or GPU cloud environment.
  • Experience optimizing GPU utilization, training performance, or distributed system reliability.
  • Familiarity with Kubernetes, containerized AI workloads, and large-scale infrastructure platforms.
Compensation
  • Competitive base pay for Bellevue market
  • Certain roles are eligible for additional rewards, including merit increases, annual bonus, and long term incentives. These awards are allocated based on individual performance
  • U.S. based employees have access to medical, dental, and vision insurance, a 401(k) plan and company match, employees also receive per calendar year, paid holidays
Location
  • Hybrid role based in the Bellevue, WA area.
  • Approximately three days per week in the office.
  • Candidates elsewhere in the U.S. who are open to relocation are encouraged to apply.
  • U.S. work authorization is required. Visa sponsorship is not currently available.
Why Join?
  • Build the infrastructure powering the next generation of AI models and applications.
  • Work directly on distributed training systems, GPU clusters, and large-scale AI platforms.
  • Solve some of the industry\'s most challenging problems around AI scalability, reliability, and efficiency.
  • Join early enough to influence architecture, tooling, and engineering practices.
  • Collaborate with a highly experienced team building critical AI infrastructure from the ground up.
  • Enjoy the ownership and technical impact of a startup environment backed by significant long-term investment.
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