Embedded Software Engineer

As an embedded software engineer at Cursor, you will work closely with customer teams to design, develop, and deploy production-grade AI systems.

Your primary responsibility will be to own an outcome, ship a production-grade SpaceXAI system, and become the critical technical feedback loop from the field back to product engineering.

Key responsibilities include:

• Leading discovery with customers to identify bottlenecks and define success metrics
• Designing and shipping production agents, AI applications, and coding-agent workflows on Grok and other models
• Getting a first version live in days and hardening it over weeks with rollout, monitoring, and iteration
• Owning production quality, including tracing, evals, debugging, and latency and cost tradeoffs
• Building systems around the model and integrating them into customer systems
• Measuring outcomes that matter, such as revenue, cost, hours saved, error rate, cycle time, and escaped defects
• Working directly with staff, platform, and domain leaders
• Leaving the customer team able to run what you built and turning what you learn into reusable patterns and improvements to SpaceXAI products

Requirements:

• 5+ years of experience in software engineering, machine learning engineering, or data science
• Proficiency in writing and reviewing production code in languages such as Python, JavaScript/TypeScript
• Experience owning a customer or operator outcome and turning a fuzzy problem into a scoped, shipped system
• Experience building and owning AI-native workflows or agents in production and debugging real production failures
• Experience handling production reliability, including metrics, alerts, safe rollouts, and incident response
• Ability to build end-to-end systems, including frontend, backend, infra, and prompt iteration
• Comfort working in ambiguity and communicating with working engineers and VPs

Nice to have:

• 2+ years of experience in a customer-facing role, leading discovery conversations and being accountable for outcomes for external stakeholders
• Hands-on depth in areas such as agents and tool-calling, eval harnesses, retrieval and context systems, CI/CD or developer platforms, and AI-native coding workflows
• Experience as a forward-deployed, solutions, or platform engineer embedded with customers, or as a founding engineer who owned an AI product end to end