Staff AI Software Engineer

SentinelOne

About The Position

At SentinelOne, we’re redefining cybersecurity by pushing the limits of what’s possible—leveraging AI-powered, data-driven innovation to stay ahead of tomorrow’s threats. From building industry-leading products to cultivating an exceptional company culture, our core values guide everything we do. We’re looking for passionate individuals who thrive in collaborative environments and are eager to drive impact. If you’re excited about solving complex challenges in bold, innovative ways, we’d love to connect with you. We’re looking for a Staff Software Engineer with strong backend fundamentals who is excited about building production AI systems. This role is a great fit for a backend SWE who works comfortably in AI-driven problem spaces and wants to apply strong software engineering skills to create LLM-backed products and platforms. This is not a research role. While we work closely with research and science teams and often operate in greenfield spaces, this position sits squarely in a product engineering organization. The focus is on designing, building, and operating reliable systems that ship real value to customers and internal users. Engineers on this team may lean more toward platform foundations (core services, APIs, enablement) or toward AI application and evaluation work (LLM-powered features, agentic workflows, quality systems), depending on strengths and interests. What unites the role is a strong commitment to software engineering rigor, stewardship, and continuous learning. Our team places a high value on experimentation and knowledge sharing. Engineers are encouraged to explore new AI tools and techniques to improve their own productivity and the quality of their work, and to share what they learn with others.

Requirements

A degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
8+ years of experience building and shipping production backend software.
Excellent modern Python engineering skills, with the ability to work effectively in distributed, asynchronous environments.
Strong system design skills, including the ability to write clear design docs and solution specifications that align stakeholders and drive sound architectural decisions.
Demonstrated ability to shepherd work from concept through production in complex, evolving environments.
Proven experience designing and implementing integrations across multiple systems in production environments.
Hands-on experience shipping and operating LLM- or generative-AI–backed features or services as part of larger production systems.
Excellent communication skills and a collaborative approach in globally distributed teams.

Nice To Haves

Experience designing or operating core platforms, internal services, or developer enablement tooling.
Experience with applied LLM systems and context engineering, including agentic workflows, retrieval-augmented generation, or evaluation pipelines.
Experience building and evolving service interfaces using REST, GraphQL, and/or gRPC, with an understanding of tradeoffs around performance, schema evolution, and compatibility.
Experience with stateless and stateful backend systems (e.g., relational or NoSQL databases, caching, streaming systems).
Experience with modern AI engineering ecosystems, such as Pydantic AI, LLM tracing and observability systems, evaluation pipelines, or MCP.
Familiarity with MLOps or AIOps concepts and tooling (e.g., MLflow, Databricks, model gateways).
Experience with cloud infrastructure (AWS, Azure, GCP), including effective use of managed services, and deployment tools (Docker, Kubernetes, Terraform, ArgoCD).
Background in—or curiosity about—applying AI to cybersecurity or similarly complex, real-world domains.

Responsibilities

Design, build, and operate backend services in Python that power AI-driven products and shared capabilities.
Build and maintain resilient service integrations across internal and external systems, handling failure modes, rate limits, and interface changes.
Own ambiguous, end-to-end problems: from early design and architecture through implementation, rollout, and iteration in production.
Develop and evolve LLM-backed features and agentic workflows used in production, with a focus on reliability, observability, and real-world behavior.
Contribute to core AI platforms and enablement systems—services that your team uses directly and that other engineers can build on.
Collaborate with product managers, researchers, and other engineers across teams to turn loosely defined AI use cases into concrete, production-ready systems.
Help shape evaluation and quality strategies for AI-powered features, including building or extending evaluation harnesses, benchmarks, or feedback loops.
Act as a technical leader for the work you own—making sound design decisions, coordinating with stakeholders, and contributing to design and code reviews.

Benefits

Medical, Vision, Dental, 401(k), Commuter, Health and Dependent FSA
Unlimited PTO
Industry-leading gender-neutral parental leave
Paid Company Holidays
Paid Sick Time
Employee stock purchase program
Disability and life insurance
Employee assistance program
Gym membership reimbursement
Cell phone reimbursement
Numerous company-sponsored events, including regular happy hours and team-building events