openai
Forward Deployed Engineer, Gov
Washington, District of Columbia
No sponsorship$146k-$280kDetected 17 days ago
JavaScriptTypeScriptPythonFull-Stack DevelopmentAWSAzureKubernetesTerraformLLMsResearchLeadership
About the role
- The OpenAI for Government team is a dynamic, mission-driven group leveraging frontier AI to transform how governments achieve their missions.
- You will embed with our most strategic government and public sector customers-where model performance matters, delivery is urgent, and ambiguity is the default.
- You'll map their problems, structure delivery, and ship fast.
Responsibilities
- Own technical delivery across multiple government deployments, from first prototype to stable production.
- Prototype and build full-stack systems using Python, JavaScript, or comparable stacks that deliver real mission impact.
- Forge and manage relationships with customer leadership and stakeholders, ensuring successful deployment and scale.
Nice to have
- Bring 5+ years of engineering or technical deployment experience, ideally in customer-facing or government environments.
Skills
- Are familiar with cloud deployment models (Azure, AWS), Kubernetes, Terraform, and related infrastructure.
Compensation
- $145.8K - $280K
Company info
- Our team works to empower public servants with secure, compliant AI tools (e.g., ChatGPT Enterprise, ChatGPT Gov) and mission-aligned deployments that meet government technical requirements with strong reliability and safety.
- Proactively guide customers on maximizing business and operational value from their applications.
- We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
- At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared.
- Deeply embed with public sector customers to design and build novel applications powered by OpenAI models.
This listing is sourced directly from openai's careers page and normalized into a canonical job model.