Senior AI Software Engineer

SentinelOne is redefining cybersecurity with AI-powered innovations. They are seeking a Senior Software Engineer to design, build, and operate backend services that power AI-driven products, focusing on reliability and collaboration across teams.

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

Requirements:

• A degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience
• 5-7+ years of experience building and shipping production backend software (Senior or Staff level, depending on experience and impact)
• 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
• 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