Job description

Our Purpose At SentinelOne, we are driven by a clear purpose: to give the advantage to those who secure our future. As AI reshapes how organizations build, operate, and innovate, the responsibility to protect them becomes more critical than ever. When you join SentinelOne, your work helps protect global enterprises, critical infrastructure, and the technologies shaping tomorrow. If you are motivated by meaningful challenges and want your impact to be real, measurable, and global, you will find purpose here. About Us SentinelOne is a company at the intersection of AI and security, pioneering a new operating model for cybersecurity. Our AI-native platform unifies protection across endpoint, cloud, identity, data, and AI systems to deliver autonomous detection and response with clarity and speed. By combining real-time analytics, intelligent automation, and a unified data foundation, we reduce noise, simplify complexity, and empower security teams to focus on what truly matters. Our teams are builders, problem-solvers, and innovators committed to shaping the future of security. If you are excited to solve hard problems alongside talented, mission-driven people, we invite you to help us build a safer future for humanity. What Are We Looking For? We’re looking for people who are relentlessly curious and committed to continuous learning. AI is reshaping every function across our business, and we enable every team member, regardless of role or level, to build fluency in AI tools and concepts. Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes. As a Staff AI Platform Engineer, you will be tasked with serving as a hands-on builder of SentinelOne's enterprise AI platform, including the Gateway, Harness, and Semantic Layers that every internal AI use case runs on top of. You will write the production code that governs, observes, secures, and scales AI activity across the company, working closely with the Sr. Director of Enterprise AI Platform Engineering and the rest of the platform team to take the architecture from design into running infrastructure. What Will You Do? Primary responsibilities include: Design, build, and operate components of the AI Gateway including centralized identity and authentication/authorization, token budgeting, DLP and content guardrails, multi-model routing and failover, MCP allow-listing, and request-level audit logging. Build and maintain pieces of the Harness layer including agent orchestration (LangGraph or equivalent), prompt construction and context management, memory and state handling across multi-turn interactions, and model abstraction across providers such as AWS Bedrock and Google Vertex. Develop and extend production services in Python (FastAPI) and pydantic.ai for LLM-powered components, and contribute to the React/TypeScript surfaces that expose platform capabilities to internal teams. Implement MCP/tool wiring for internal and SaaS-embedded agents, ensuring every caller regardless of origin passes through the same policy controls. Contribute to the Claude and Gemini Enterprise plugin framework by building, testing, and hardening role-based skills and agents, and help mature the pipeline for how plugins are authored, evaluated, and deployed. Instrument the platform for observability including metrics, tracing, and audit trails, and participate in on-call and operational support for platform services. Write clear technical documentation and participate in architecture and code reviews, holding a high bar for quality, security, and maintainability. Partner with engineers across Enterprise Data, Enterprise Apps, Product Development, and Infosec to integrate the platform with governed data sources and shared architectural contracts. Work with the Sr. Director and model evaluation tooling to help close the loop between evaluation results and model selection, prompt tuning, and routing decisions. What Skills and Knowledge Will You Bring? Ideal candidates will have: Hands-on experience building production services that sit in front of multiple consumers such as an API gateway, internal platform, or data platform, with real exposure to authentication/authorization, rate limiting, observability, or audit logging; direct AI and LLM platform experience is a strong plus but not required. Practical experience with agent orchestration frameworks (LangGraph or equivalent) and calling hosted model providers such as AWS Bedrock or Google Vertex, with a working understanding of how context windows, memory, and tool-calling actually behave in production, not just conceptually. 5 or more years of professional software engineering experience; with experience building or operating AI and ML infrastructure in a production environment. Strong Python skills, ideally with FastAPI, and comfort picking up frameworks like pydantic.ai for LLM-powered components; working familiarity with React and TypeScript is a plus. Exposure to or curiosity about the Model Context Protocol (MCP) and the challenges of governing tool access for agents operating across enterprise systems. Familiarity with enterprise AI deployments such as Claude Enterprise (Anthropic) or Gemini Enterprise is a plus, including how they are administered and how access and policy controls work. Solid software engineering fundamentals including clean, tested, production-grade code, strong API design skills, and the ability to give and receive feedback well in code and architecture reviews. Comfort operating in a fast-moving, still-forming platform environment where you are energized by ambiguity and enjoy turning a rough architecture into working, reliable infrastructure. Why SentinelOne? AI is redefining how the world operates and rewriting the rules of security in real time, and SentinelOne was built for this moment. From day one, we architected an AI-native platform designed to operate at machine speed, not as an add-on to legacy systems but as the foundation itself. If you want to build where innovation and impact move together, this is that place. We invest in our Sentinels with comprehensive, competitive benefits designed to support you and your family: Equity & Rewards Restricted Stock Units (RSUs) Employee Stock Purchase Plan (ESPP) Time Off & Wellbeing Flexible time off Paid company holidays and paid sick time Gender-neutral parental leave Grandparent leave Insurance & Financial Security Medical, dental, and vision coverage 401(k) retirement plan with company match Life and disability insurance Health and dependent care FSA Voluntary benefits (hospital, accident, critical illness) Employee Assistance Program (EAP) ARAG pre-paid legal Nationwide pet insurance Cancer Care program Global business travel medical insurance Work Perks & Flexibility Home office allowance Mobile phone reimbursement Wellness & Lifestyle Wellness coach Wellness/gym reimbursement Fertility coverage Adoption & surrogacy reimbursement This U.S. role has a base pay range that will vary based on the location of the candidate. For some locations, a different pay range may apply. If so, this range will be provided to you during the recruiting process. You can also reach out to the recruiter with any questions. Base Salary Range $156,000 — $215,000 USD SentinelOne is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. SentinelOne participates in the E-Verify Program for all U.S. based roles.

Requirements

Must-haves

Develop and extend production services in Python (FastAPI) and pydantic.ai for LLM-powered components, and contribute to the React/TypeScript surfaces that expose platform capabilities to internal teams.
Implement MCP/tool wiring for internal and SaaS-embedded agents, ensuring every caller regardless of origin passes through the same policy controls.
Contribute to the Claude and Gemini Enterprise plugin framework by building, testing, and hardening role-based skills and agents, and help mature the pipeline for how plugins are authored, evaluated, and deployed.
Instrument the platform for observability including metrics, tracing, and audit trails, and participate in on-call and operational support for platform services.
Write clear technical documentation and participate in architecture and code reviews, holding a high bar for quality, security, and maintainability.
Partner with engineers across Enterprise Data, Enterprise Apps, Product Development, and Infosec to integrate the platform with governed data sources and shared architectural contracts.
Work with the Sr. Director and model evaluation tooling to help close the loop between evaluation results and model selection, prompt tuning, and routing decisions.
What Skills and Knowledge Will You Bring?
5 or more years of professional software engineering experience; with experience building or operating AI and ML infrastructure in a production environment.
Exposure to or curiosity about the Model Context Protocol (MCP) and the challenges of governing tool access for agents operating across enterprise systems.
Solid software engineering fundamentals including clean, tested, production-grade code, strong API design skills, and the ability to give and receive feedback well in code and architecture reviews.
Comfort operating in a fast-moving, still-forming platform environment where you are energized by ambiguity and enjoy turning a rough architecture into working, reliable infrastructure.
Why SentinelOne?
We invest in our Sentinels with comprehensive, competitive benefits designed to support you and your family:
Equity & Rewards
Restricted Stock Units (RSUs)
Employee Stock Purchase Plan (ESPP)
Time Off & Wellbeing
Flexible time off
Grandparent leave
Insurance & Financial Security
Life and disability insurance

Nice-to-haves

Strong Python skills, ideally with FastAPI, and comfort picking up frameworks like pydantic.ai for LLM-powered components; working familiarity with React and TypeScript is a plus.
Familiarity with enterprise AI deployments such as Claude Enterprise (Anthropic) or Gemini Enterprise is a plus, including how they are administered and how access and policy controls work.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

airestawspythonllmreacttypescriptapiml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Parental Leave

Wellness