Artificial Intelligence Testing Jobs in Washington

• Model performance gets validated against real operational conditions, not only against a clean benchmark dataset.
• Where a model is wrong, you can say how it is wrong and how often, rather than reporting a single accuracy number and stopping there.
• You can explain a model's behavior to someone who did not build it, in terms they can evaluate.

• Model behavior gets monitored after deployment, not just validated once before it.
• Drift and degraded performance get caught before they change an operational outcome.
• Failure modes are documented and understood before a model goes live, not discovered afterward.

• Documentation and evidence for a security or accreditation review come out of your normal development process.
• You can tell an engineering team early what an AI capability will need to clear review, before they have built around an assumption that will not survive it.
• Reviewers get a straight answer about what a model does and does not do.

• Other engineers and program staff come to you before committing to an AI approach, not after it has already been built.
• Where AI is not the right tool for a problem, you can say so and be heard.
• Standards for evaluating and deploying models exist and get used on the next project, not reinvented each time.

• 5+ years in ML, Data Science, or AI engineering
• 2+ years focused on Generative AI and LLMs
• Deep expertise in machine learning, deep learning, and statistical modeling 
• Hands-on experience with LLM frameworks, vector databases, and prompt engineering
• Advanced Python proficiency; production-grade software development experience
• MLOps and cloud AI platform experience (Azure AI, AWS Bedrock, Google Vertex AI)
• Proven track record taking AI solutions from PoC to production
• Experience in assessing cyber risk and functionality of AI solutions
• Experience in incorporating automation and artificial intelligence into cyber prpgrams
• Experience with data analytic software such as Splunk, Cribl, Elastic and Sentinel

• Open-source contributions or published AI research
• Experience with edge/low-latency model optimization
• Certifications in AI Ethics, Responsible AI, or Cloud AI Architecture
• Familiarity with adversarial AI testing and red-teaming
• You have taken a machine learning model from prototype to a production system that real people depended on.
• You have worked in a regulated or federal environment where a model's decisions had to be explainable and defensible, not only accurate.
• You have caught a model failure before it reached a decision that mattered, and can describe how.
• You are comfortable across the model lifecycle: data, training, validation, deployment, and monitoring, rather than specialized in only one stage.
• You hold an active CBP BI, a fitness determination at another DHS component, or an active DoD clearance. Any of these shortens your start date.
• You can talk about the limits of a model as clearly as you talk about its capabilities.

• Happy - Be Infectious. Happiness multiplies and creates a positive and connected environment where motivation and satisfaction have an outsized effect on everything we do.
• Helpful - Be Supportive. Being helpful is the foundation of teamwork, resulting in a supportive atmosphere where collaboration flourishes, and collective success is celebrated.
• Honest - Be Trustworthy. Honesty serves as our compass, ensuring transparent communication and ethical conduct, essential to who we are and the complex domains we support.
• Humble - Be Grounded. Success is not achieved alone, humility ensures a culture of mutual respect, encouraging open communication, and a willingness to learn from one another and take on any task.
• Hungry - Be Eager. Our hunger for excellence drives an insatiable appetite for innovation and continuous improvement, propelling us forward in the face of new and unprecedented challenges.
• Hustle - Be Driven. Hustle is reflected in our relentless work ethic, where we are each committed to going above and beyond to advance the mission and achieve success.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.