Senior Software Engineering IC4

Microsoft•,

4d•$119,800 - $261,000

About The Position

Microsoft Security Studio is seeking a Software Engineer to build AI-infused security solutions with customers at the intersection of AI, cloud, and cybersecurity. In a Forward Deployed Engineering (FDE) model, the engineer works directly with customer engineers, security teams, and Microsoft product teams to identify high-value problems, shape solutions, and deliver secure, production-ready software using Microsoft platforms, AI and agentic systems, and custom engineering as needed. AI is central to both the engineering approach and the solutions being built. The role applies AI-assisted practices throughout the development lifecycle and creates intelligent security capabilities that reason over complex environments, automate workflows, assist defenders, and act safely on their behalf. The engineer owns the full lifecycle, from customer discovery, architecture, and success criteria through implementation, AI evaluation, deployment, operation, and handoff of maintainable code and an operable solution. The role also turns engagement insights into reusable assets, accelerators, patterns, and product improvements. Work spans security scenarios including security operations, vulnerability discovery and remediation, secure software development, identity, governance, and AI security. The position combines startup-like urgency, ownership, and experimentation with Microsoft’s engineering depth, product portfolio, and global customer reach.

Requirements

Bachelor’s degree in Computer Science or a related technical field and 4+ years of technical engineering experience coding in one or more languages such as C, C++, C#, Java, JavaScript, TypeScript, Go, Rust, or Python; or equivalent experience.
Demonstrated experience designing, building, testing, deploying, debugging, and operating substantial production software or cloud services.
Strong software-engineering fundamentals, including data structures, algorithms, APIs, distributed systems, cloud architecture, testing, reliability, and maintainable design.
Experience translating ambiguous, evolving customer or stakeholder needs into architecture, engineering plans, working software, and measurable outcomes.
Practical knowledge of secure software engineering, including authentication and authorization, identity, secrets management, threat modeling, data protection, secure dependencies, vulnerability remediation, and monitoring.
Ability to explain complex technical concepts, architecture decisions, trade-offs, and delivery risks to technical and nontechnical audiences.

Nice To Haves

Master’s degree in Computer Science or a related technical field and 6+ years of technical engineering experience; or Bachelor’s degree and 8+ years of technical engineering experience; or equivalent experience.
Strong hands-on experience building and operating AI- or LLM-based applications, agents, or developer systems, including orchestration, grounding/RAG, structured tool use, evaluation, observability, safety controls, and production monitoring.
Experience building agentic systems that interact with enterprise data, APIs, tools, or workflows, with attention to identity, authorization, auditability, data boundaries, and safe execution.
Experience using AI-assisted development and coding agents across the engineering lifecycle.
Experience building cybersecurity products or solutions in areas such as security operations, vulnerability management, identity, endpoint or cloud security, software supply-chain security, automation, or AI security.
Experience with cloud-native architecture, distributed systems, containers, infrastructure as code, CI/CD, observability, reliability engineering, and live-site operations.
Knowledge of AI security and security for AI, including prompt-injection defenses, sensitive-data handling, grounding controls, model or agent evaluation, responsible AI, and safe tool execution.
Demonstrated ability to move from customer problem to prototype to production solution to reusable engineering asset while balancing speed, quality, security, and maintainability.
Experience partnering with product engineering teams to turn customer scenarios and technical blockers into actionable requirements, prototypes, or validation evidence.
Ability to work effectively in small, high-ownership multidisciplinary teams and with senior customer engineering and security leaders while remaining deeply hands-on.
Willingness and ability to travel up to 25%.

Responsibilities

Design and build AI-infused security software with customers, including cloud services, intelligent applications, security automation, agents, developer tooling, integrations, and distributed systems.
Embed with customer engineering and security teams to understand desired outcomes, architecture, threat models, data, and operational constraints, then translate ambiguity into executable engineering plans.
Co-develop in short, iterative cycles, demonstrating working software and using real-user feedback, evaluations, telemetry, and operational data to refine the solution and measure success.
Own production-quality delivery from prototyping and architecture through implementation, automated testing, CI/CD, infrastructure as code, deployment, observability, troubleshooting, documentation, operational readiness, and handoff.
Use AI-assisted engineering across the lifecycle and build agentic systems that safely access enterprise data and tools, with appropriate human review, identity, authorization, data boundaries, isolation, auditability, guardrails, and accountability.
Apply secure-by-design and privacy-by-design principles, including threat modeling, least privilege, secrets and identity management, data protection, dependency and vulnerability management, and secure operations.
Convert customer-specific learning into reusable components, accelerators, reference implementations, engineering patterns, paved paths, and actionable product feedback.
Lead significant technical workstreams by setting direction, decomposing ambiguous problems, making architecture decisions, managing dependencies, risks, and trade-offs, and communicating clearly with technical and senior stakeholders.
Collaborate in small multidisciplinary teams, learn rapidly, experiment responsibly, challenge assumptions constructively, and share knowledge across engagements.