Founding Backend Engineer

Wingback Security is building intent-to-execution security for AI agents, models, and MCP connectors. Our platform helps organizations understand what AI systems are doing, monitor actions across agentic workflows, detect deviations from expected behavior, and mitigate risks as AI adoption scales.

We are focused on securing the next generation of enterprise AI infrastructure: autonomous agents, tool-calling systems, model-connected applications, and the growing ecosystem of MCP-based integrations. Wingback helps businesses deploy AI with confidence by providing visibility, control, and security across dynamic AI environments.

We are looking for a Founding Backend Engineer to help build the core platform from the ground up. This is an early engineering role for someone who wants to work close to the problem, shape technical direction, move fast, and build foundational systems that will define the company.

You will work across backend architecture, distributed systems, security telemetry, AI workflow analysis, product infrastructure, and developer tooling. You should be comfortable operating with high autonomy, making sound technical decisions with limited supervision, and turning ambiguous product and security problems into working systems.

This role is ideal for an engineer who has worked in early-stage startups before, understands the pace and ambiguity of company-building, and wants to build deeply technical security infrastructure for the AI era.

You will:

• Design and build backend services for Wingback’s AI security platform.
• Develop systems for monitoring AI agent actions, MCP connector activity, model interactions, tool calls, and execution flows.
• Build scalable data pipelines for collecting, normalizing, analyzing, and storing security-relevant AI telemetry.
• Design APIs, services, and internal frameworks that support real-time detection, policy enforcement, and risk analysis.
• Work closely with the founder to translate product vision, customer problems, and security research into production-ready capabilities.
• Own large parts of the backend architecture, including service design, data models, queues, workers, databases, integrations, and deployment patterns.
• Build internal and customer-facing integrations with AI platforms, agent frameworks, cloud environments, and enterprise systems.
• Use AI coding assistants and agentic development tools with high precision to accelerate engineering velocity.
• Define and maintain developer workflows, agent-assisted coding patterns, automated test suites, CI/CD pipelines, and quality gates.
• Help establish engineering standards for security, reliability, observability, and maintainability.
• Operate comfortably in a fast-moving startup environment where priorities evolve quickly.

Required:

• Strong backend engineering experience, ideally in Python, Go, TypeScript, Java, or similar production backend stacks.
• Prior experience as an early engineer, founding engineer, or senior backend engineer in an early-stage startup.
• Ability to work with autonomy, ownership, and minimal supervision.
• Strong system design skills, especially for distributed backend services, APIs, async workers, event-driven systems, and data pipelines.
• Experience building production systems with databases, queues, cloud infrastructure, observability, and deployment automation.
• Strong understanding of software engineering fundamentals: testing, debugging, performance, reliability, and secure coding.
• Ability to take ambiguous requirements and turn them into clear technical plans and shipped product.
• High comfort with AI coding assistants, developer agents, and automation tools.
• Ability to drive AI coding tools precisely: defining implementation plans, code-generation tasks, test suites, agent skills, review workflows, and automated validation pipelines.

Nice to Have:

• Experience working at a security company or building security products.
• Familiarity with AI security, LLM applications, AI agents, MCP, tool-calling systems, or prompt-injection risks.
• Experience with cloud security, identity systems, policy engines, security telemetry, detection engineering, or runtime protection.
• Experience building developer platforms, observability systems, SIEM-like pipelines, API gateways, or infrastructure security tools.
• Familiarity with AWS, Kubernetes, Terraform, Docker, Postgres, Kafka, Redis, OpenTelemetry, or similar technologies.
• Experience designing automated evaluation pipelines, test harnesses, or simulation environments for AI systems.
• Comfort working directly with customers, design partners, or security teams to understand technical requirements.

You might be a great fit if you:

• Have been an early engineer before and understand that the job is not just writing code, but building the engineering foundation of a company.
• Are comfortable making architectural decisions without waiting for perfect requirements.
• Can move quickly without creating unnecessary technical debt.
• Have strong opinions on backend architecture, testing, automation, and developer workflows.
• Use AI coding tools as a force multiplier, not as a shortcut.
• Care deeply about security, reliability, and correctness.
• Want to work on one of the most important emerging problems in enterprise security: securing agentic AI systems from intent to execution.

• Build at the frontier of AI security, agents, and MCP infrastructure.
• Join at the founding stage and directly shape the product, architecture, and engineering culture.
• Work on technically deep problems involving AI behavior, runtime security, telemetry, policy, and enterprise-scale systems.
• Have significant ownership from day one.
• Help define how companies secure AI systems as they move from chat interfaces to autonomous execution.