AI Safety Operator Jobs
AI Safety Operator jobs are open across AI research labs, tech companies, defense contractors, and financial institutions, from junior operator to senior and principal levels, with specializations in model monitoring, red-teaming, and incident response. See the openings below and apply to the ones that match your experience.
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Karman Space & Defense is a leader in the rapid design, development, and production of critical, next-generation system solutions that align with the U.S. Department of War and its allies' core mission priorities, and meet the accelerating demand for access to space. Building on nearly 50 years of success, we deliver Payload & Protection Systems, Aero/Hydrodynamic Interstage Systems, and Propulsion & Launch Systems to more than 80 prime contractors supporting over 130 space and defense programs.
In this senior individual‑contributor role, you will translate Karman's enterprise artificial intelligence (AI) strategy into secure, scalable, and reusable architectures that accelerate responsible adoption across the organization. You will shape technical patterns, build reference implementations, evaluate emerging technologies, and establish guardrails that enable governed self‑service. Partnering closely with AI/Data & Automation, Business Systems, Cybersecurity, Infrastructure, and functional teams, you will provide hands‑on technical leadership that strengthens solution quality, operational consistency, and enterprise readiness.
Responsibilities
- Defines and evolves Karman's enterprise AI reference architecture.
- Establishes architectural patterns for model access, retrieval‑augmented generation (RAG), orchestration, agents, application programming interfaces (APIs), evaluation, observability, and cost management.
- Develops technical standards for Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and other approved enterprise AI capabilities.
- Evaluates external platforms and models based on security, performance, cost, and business needs.
- Designs architectures that support portability using APIs, abstraction layers, open protocols, and exit strategies.
- Builds proofs of concept, reference implementations, reusable components, and technical accelerators.
- Provides hands‑on support for complex AI use cases, including RAG solutions, agentic workflows, model‑integrated applications, and automation.
- Establishes reusable templates, patterns, guardrails, and documentation to enable governed self‑service.
- Translates enterprise AI governance requirements into technical controls for identity, access, logging, monitoring, classification, retention, and human oversight.
- Defines evaluation, observability, quality, operational readiness, incident response, and lifecycle management standards for AI solutions.
Required Qualifications
- Significant experience in enterprise architecture, cloud architecture, software engineering, or platform engineering.
- Recent experience designing and deploying production generative artificial intelligence (AI) solutions.
- Strong working knowledge of the Microsoft cloud and AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Entra ID, Purview, and Microsoft security technologies.
- Hands‑on experience integrating commercial AI models through APIs and building model‑enabled applications.
- Strong understanding of RAG architectures, agent patterns, tool use, model orchestration, vector search, and enterprise data integration.
- Familiarity with standards and technologies such as Model Context Protocol (MCP), OpenTelemetry, open‑weight models, and modern inference platforms.
- Experience designing secure systems in regulated or high‑assurance environments.
- Ability and willingness to write code, build prototypes, review designs, and troubleshoot technical issues.
- Strong written and verbal communication skills with the ability to explain complex technical tradeoffs to technical and executive audiences.
Preferred Qualifications
- Experience in aerospace, defense, government, manufacturing, financial services, or other regulated industries.
- Experience with GCC High, sovereign cloud, restricted environments, or controlled‑data architectures.
- Familiarity with frameworks such as Cybersecurity Maturity Model Certification (CMMC), National Institute of Standards and Technology (NIST) Special Publication 800‑171, Controlled Unclassified Information (CUI), International Traffic in Arms Regulations (ITAR), NIST AI Risk Management Framework (RMF), and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001.
- Experience integrating AI with enterprise platforms such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationship management (CRM), productivity, analytics, and workflow systems.
- Familiarity with graphics processing unit (GPU) infrastructure, self‑hosted models, fine‑tuning, and private inference environments.
- Relevant Microsoft, cloud, architecture, or security certifications.
This position requires U.S. person status under U.S. export control laws, including U.S. citizens and nationals, lawful permanent residents, refugees, and asylees.
Benefits
- Medical, dental, and vision insurance
- 401(k) with company match
- Paid time off
- Health Savings Account (HSA) with company contribution
- Flexible Spending Accounts (FSA)
- Company‑paid life and ADdummyamp;D insurance
- Short‑ and long‑term disability coverage
- Tuition reimbursement
Karman Space and Defense is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, protected veteran status, or any other status protected by applicable federal, state, or local law.
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Karman Space & Defense is a leader in the rapid design, development, and production of critical, next-generation system solutions that align with the U.S. Department of War and its allies' core mission priorities, and meet the accelerating demand for access to space. Building on nearly 50 years of success, we deliver Payload & Protection Systems, Aero/Hydrodynamic Interstage Systems, and Propulsion & Launch Systems to more than 80 prime contractors supporting over 130 space and defense programs.
In this senior individual‑contributor role, you will translate Karman's enterprise artificial intelligence (AI) strategy into secure, scalable, and reusable architectures that accelerate responsible adoption across the organization. You will shape technical patterns, build reference implementations, evaluate emerging technologies, and establish guardrails that enable governed self‑service. Partnering closely with AI/Data & Automation, Business Systems, Cybersecurity, Infrastructure, and functional teams, you will provide hands‑on technical leadership that strengthens solution quality, operational consistency, and enterprise readiness.
Responsibilities
- Defines and evolves Karman's enterprise AI reference architecture.
- Establishes architectural patterns for model access, retrieval‑augmented generation (RAG), orchestration, agents, application programming interfaces (APIs), evaluation, observability, and cost management.
- Develops technical standards for Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and other approved enterprise AI capabilities.
- Evaluates external platforms and models based on security, performance, cost, and business needs.
- Designs architectures that support portability using APIs, abstraction layers, open protocols, and exit strategies.
- Builds proofs of concept, reference implementations, reusable components, and technical accelerators.
- Provides hands‑on support for complex AI use cases, including RAG solutions, agentic workflows, model‑integrated applications, and automation.
- Establishes reusable templates, patterns, guardrails, and documentation to enable governed self‑service.
- Translates enterprise AI governance requirements into technical controls for identity, access, logging, monitoring, classification, retention, and human oversight.
- Defines evaluation, observability, quality, operational readiness, incident response, and lifecycle management standards for AI solutions.
Required Qualifications
- Significant experience in enterprise architecture, cloud architecture, software engineering, or platform engineering.
- Recent experience designing and deploying production generative artificial intelligence (AI) solutions.
- Strong working knowledge of the Microsoft cloud and AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Entra ID, Purview, and Microsoft security technologies.
- Hands‑on experience integrating commercial AI models through APIs and building model‑enabled applications.
- Strong understanding of RAG architectures, agent patterns, tool use, model orchestration, vector search, and enterprise data integration.
- Familiarity with standards and technologies such as Model Context Protocol (MCP), OpenTelemetry, open‑weight models, and modern inference platforms.
- Experience designing secure systems in regulated or high‑assurance environments.
- Ability and willingness to write code, build prototypes, review designs, and troubleshoot technical issues.
- Strong written and verbal communication skills with the ability to explain complex technical tradeoffs to technical and executive audiences.
Preferred Qualifications
- Experience in aerospace, defense, government, manufacturing, financial services, or other regulated industries.
- Experience with GCC High, sovereign cloud, restricted environments, or controlled‑data architectures.
- Familiarity with frameworks such as Cybersecurity Maturity Model Certification (CMMC), National Institute of Standards and Technology (NIST) Special Publication 800‑171, Controlled Unclassified Information (CUI), International Traffic in Arms Regulations (ITAR), NIST AI Risk Management Framework (RMF), and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001.
- Experience integrating AI with enterprise platforms such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationship management (CRM), productivity, analytics, and workflow systems.
- Familiarity with graphics processing unit (GPU) infrastructure, self‑hosted models, fine‑tuning, and private inference environments.
- Relevant Microsoft, cloud, architecture, or security certifications.
This position requires U.S. person status under U.S. export control laws, including U.S. citizens and nationals, lawful permanent residents, refugees, and asylees.
Benefits
- Medical, dental, and vision insurance
- 401(k) with company match
- Paid time off
- Health Savings Account (HSA) with company contribution
- Flexible Spending Accounts (FSA)
- Company‑paid life and ADdummyamp;D insurance
- Short‑ and long‑term disability coverage
- Tuition reimbursement
Karman Space and Defense is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, protected veteran status, or any other status protected by applicable federal, state, or local law.
View our Privacy Policy
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Find AI Safety Operator JobsAI Safety Operator Job Market
Who's Hiring
- Booz Allen Hamilton20
- JPMorganChase11
- Johns Hopkins University Applied Physics Laboratory9
- Tesla6
- University of San Diego6
Top Industries Hiring
- Education
- Technology & Software
- Banking & Financial Services
- Healthcare & Medical Services
- Insurance
What Employers Look For
The qualifications that appear most often in AI safety operator jobs.
- Experience monitoring large language model outputs for harmful or policy-violating content
- Familiarity with safety evaluation frameworks such as red-teaming and adversarial prompt testing
- Ability to triage, document, and escalate model incidents according to defined severity levels
- Background in trust and safety, content moderation, or AI quality assurance workflows
- Strong written communication skills for drafting incident reports and safety documentation
- Bachelor's degree in computer science, cognitive science, linguistics, or a related field preferred
Tips for Your AI Safety Operator Job Search
Tailor your resume to safety workflows
Highlight hands-on experience with model evaluation, anomaly detection, or policy enforcement rather than general AI knowledge. Hiring managers for ai safety operator roles scan for evidence you have worked inside a safety review pipeline, not just studied one.
Apply early to roles that fit
Migrate Mate lists ai safety operator openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Document your red-teaming contributions clearly
Many candidates omit specifics about adversarial testing or prompt-injection exercises because they seem niche. Write those out explicitly, since ai safety operator job descriptions routinely screen for red-teaming exposure and your resume needs to reflect it word for word.
Filter openings by deployment context
AI safety operator roles at model providers differ sharply from those at enterprises deploying third-party models. Identify which context matches your background before applying, so your cover letter addresses the specific safety framework the team actually uses.
Prepare for scenario-based technical screens
Interviewers will walk you through a live model output and ask how you would classify and escalate it. Practice narrating your decision logic aloud, because interviewers are evaluating your reasoning process as much as your final classification call.
Negotiate scope alongside compensation
AI safety operator roles vary widely in what you are authorized to escalate or shut down. During offer discussions, clarify your incident-response authority in writing, since ambiguous escalation limits create real operational problems once you are on the job.
AI Safety Operator Jobs: Frequently Asked Questions
Which companies are hiring the most ai safety operators?
The most active employers for ai safety operators right now are Booz Allen Hamilton, JPMorganChase, and Johns Hopkins University Applied Physics Laboratory, and the most openings are in Virginia, California, and Texas, based on current listings on Migrate Mate as of November 2026. Demand is concentrated at AI research labs, large technology platforms, and defense-adjacent contractors building or deploying frontier models.
How many ai safety operator jobs are remote?
About 53% of ai safety operator openings are fully remote or hybrid as of November 2026, reflecting how much of the work can be done through web-based evaluation tools and asynchronous review queues. Roles focused on content and output review tend to be the most remote-accessible, while positions requiring hands-on model access in a secure environment are more likely to be on-site.
How do you become an ai safety operator?
Start by building direct exposure to AI outputs, through content moderation roles, trust and safety positions, or academic research on model evaluation. Develop fluency with prompt testing and document any structured work you have done classifying or escalating harmful outputs. Certifications in AI ethics or responsible AI development strengthen your application, and contributing to open safety benchmarks or red-teaming competitions demonstrates practical readiness to hiring teams.
Can you get hired as an ai safety operator with little or no experience?
Yes, entry-level ai safety operator roles exist and typically prioritize strong analytical judgment and clear written communication over years of direct experience. Candidates who have done content moderation, academic AI research, or structured red-teaming exercises, even informally, are competitive. Building a portfolio of documented model evaluations or safety analyses and applying to junior operator openings at companies running active safety programs gives you a realistic path in.
What does the ai safety operator interview process look like?
Most ai safety operator interview processes include an initial screening call, a written or recorded scenario exercise where you evaluate and classify a set of model outputs, and a final round with the safety team that tests your reasoning and escalation judgment. Some employers add a policy comprehension stage where you apply the company's internal safety guidelines to ambiguous cases. The process generally runs across two to four rounds depending on team size.
Where can I find and apply to ai safety operator jobs?
You can find and apply to ai safety operator jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your background and experience level, then apply directly to each one that fits. All applications go straight to the employer so you can move through the process without leaving the platform.
See All 303+ AI Safety Operator Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI Safety Operator Jobs