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Model Validation Remote Jobs in Bothell, WA (NOW HIRING)

Waymo

Software Engineer, Model Lifecycle

Kirkland, WA · On-site +1

$204K - $259K/yr

Automate data quality and validation checks to ensure the integrity, consistency, and ... remote, the specific salary range for your preferred location, during the hiring process. Waymo ...

Relativity

Principal Applied Scientist

Seattle, WA · On-site +1

Posting Type Remote/Hybrid Job Overview WHO WE ARE Relativity is a leading legal data intelligence ... Validate model behavior rigorously using evidence, metrics, and experimentation, remaining open to ...

Alignerr

Development Economist

Seattle, WA · Remote

This is a fully remote, flexible contract role where your knowledge makes a tangible difference ... Apply your academic or real-world experience to validate economic scenarios and policy reasoning

DataAnnotation

Cloud Security Engineer

Renton, WA · Remote

$40 - $75/hr

Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand ... Provide feedback that directly shapes the next generation of AI security models. Qualifications ...

DataAnnotation

AI Security Specialist

Seattle, WA · Remote

$40 - $75/hr

Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand ... Provide feedback that directly shapes the next generation of AI security models. Qualifications ...

DataAnnotation

AI Security Specialist

Everett, WA · Remote

$40 - $75/hr

Fully remote: work from anywhere in the US, Canada, UK, Ireland, Australia, and New Zealand ... Provide feedback that directly shapes the next generation of AI security models. Qualifications ...

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Model Validation Remote information

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$25

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$87

How much do model validation remote jobs pay per hour?

As of May 30, 2026, the average hourly pay for model validation remote in Bothell, WA is $58.13, according to ZipRecruiter salary data. Most workers in this role earn between $44.09 and $70.67 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Model Validation Remote, and why are they important?

To thrive as a Model Validation Remote, you need a strong background in quantitative disciplines such as mathematics, statistics, or finance, typically supported by a relevant degree. Proficiency with statistical software (like SAS, R, or Python), model risk management frameworks, and familiarity with regulatory guidelines (such as SR 11-7) are commonly required. Analytical thinking, attention to detail, and strong written communication are crucial soft skills in this role. These skills ensure accurate model assessments, regulatory compliance, and effective communication of complex findings to stakeholders.

What are some common challenges faced by professionals in remote model validation roles, and how can they be addressed?

Remote model validation professionals often encounter challenges such as maintaining clear communication with model developers and stakeholders, accessing secure data environments, and staying updated with evolving regulatory standards. To address these, it's important to leverage robust collaboration tools, schedule regular check-ins with cross-functional teams, and participate in ongoing training or knowledge-sharing sessions. Establishing clear documentation protocols and ensuring secure remote access to necessary data can also help maintain productivity and compliance.

What is model validation in a remote job context?

Model validation, especially in a remote setting, involves evaluating and verifying the accuracy, performance, and reliability of statistical or machine learning models from a location outside of a traditional office. Professionals in this role typically assess whether models meet regulatory requirements, function as intended, and are free from biases or errors. Remote model validators use various tools and techniques to conduct tests, write reports, and communicate findings with stakeholders via digital platforms. This work is essential in sectors like finance, insurance, and tech, where robust models drive critical decisions. Successful remote model validation requires strong analytical skills, clear communication, and proficiency with data analysis tools.

What is the difference between Model Validation Remote vs Model Validation on-site?

AspectModel Validation RemoteModel Validation on-site
Work EnvironmentRemote, home-basedOn-site, office or client location
Required CredentialsSimilar certifications, e.g., CFA, FRM, or relatedSame as remote, often with additional in-person requirements
Industry UsageFinancial institutions, banks, asset managersSame industries, with in-person collaboration
Work FlexibilityHigh, flexible hours and locationLess flexible, fixed hours and location

Both remote and on-site model validation roles require similar credentials and industry knowledge. The main difference lies in the work environment and flexibility, with remote positions offering greater convenience and location independence, while on-site roles facilitate direct collaboration and immediate access to resources.

What are the most commonly searched types of Model Validation jobs in Bothell, WA? The most popular types of Model Validation jobs in Bothell, WA are:
What cities near Bothell, WA are hiring for Model Validation Remote jobs? Cities near Bothell, WA with the most Model Validation Remote job openings:
Model Validation Remote jobs near you
Software Engineer, Model Lifecycle

Software Engineer, Model Lifecycle

Waymo

Kirkland, WA • On-site, Remote

$204K - $259K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 14 days ago

Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver-The World's Most Experienced Driver-to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo's fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

The core challenge within Model Lifecycle is accelerating Waymo's ML development cycle. As we scale to new cities and vehicle platforms, our data volume is exploding, and our models are becoming more complex, handling more and more tasks. This team is critical to controlling that complexity.

In this hybrid role, you will report to an engineering manager.

You will:

  • Design, build, and maintain scalable data pipelines to process many petabytes of complex sensor data, making it ready for efficient model training and evaluation.
  • Develop infrastructure to produce reliable, high-quality datasets for a wide range of ML models, from real-time on-car models to large-scale offboard foundation models.
  • Build towards an automated, unified data flywheel -- a datagen and ingestion solution that seamlessly connects data curation to model training.
  • Develop infrastructure for Perception-wide model training and release-ready packaging, ensuring the model development lifecycle is robust, efficient, and reproducible.
  • Maintain and support critical data generation infrastructure and data refreshes for the Perception team.
  • Automate data quality and validation checks to ensure the integrity, consistency, and trustworthiness of our datasets as we scale to new cities and vehicle platforms
  • Collaborate closely with ML engineers, research scientists, and core infrastructure teams to understand user needs and deliver impactful ML workflows.

You have:

  • Outstanding programming skills in C++ or Python
  • Experience in ML data engineering, including data pipelines, data curation, data balancing, etc.
  • Experience with the ML development lifecycle, including data engineering, model training, model evaluation, and model deployment.
  • BS/MS and 5+ years of industry experience, or PhD + 2 years of industry experience
  • Passionate about data-centric AI and autonomous driving applications

We prefer:

  • Experience in working in cross-functional settings to support data users and collaborating with infrastructure stakeholders; customer-oriented mindset
  • Hands-on experience in building large scale data processing or retrieval systems and pipelines: Apache Spark, Apache Beam, Google Cloud Dataflow, AWS Data Pipeline, Faiss/ScaNN, etc.
  • Experience building automated ML pipelines -- data pipelines, continuous model training/evaluation pipelines, etc.

In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:

  • Health, dental, vision, life, disability insurance
  • Retirement Benefits: 401(k) with company match
  • Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
  • Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)
  • Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
  • Baby Bonding Leave: 18 weeks
  • Holidays: 13 paid days per year

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.

Salary Range
$204,000—$259,000 USD

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