Forward Deployed Engineer

Cursor
San Francisco, CA
On-site

Who this role is best for

Aimed at senior software engineers or ML engineers with hands-on production experience who thrive in ambiguity and can bridge customer needs with technical execution.

Best fit for

  • Senior engineers with end-to-end system ownership and experience embedding with customers to deliver measurable impact
    — “You own the work end to end, from the first discovery call through launch, iteration, and post-launch support”
  • Individuals with a track record of shipping production-grade systems and translating vague problems into concrete solutions
    — “You’ve owned a customer or operator outcome, not just a slide or a prototype, and can turn a fuzzy problem into a scoped, shipped system”

Things to consider

  • The role demands a high level of autonomy and the ability to work with minimal upfront specification
    — “You thrive in ambiguity and don’t need a complete spec before you start building”
  • Candidates must be comfortable working directly with both engineers and VPs, requiring strong communication across levels
    — “Work directly with Staff+, platform, and domain leaders, going deep in their systems while communicating clearly about tradeoffs and results”
  • The role requires a commitment to long-term customer engagement and iterative improvement based on real usage
    — “Get a first version live in days, then harden it over weeks with rollout, monitoring, and iteration from real usage”

How to stand out

  • Highlight specific examples of shipping production systems that solved real business bottlenecks
    — “You’ve owned a customer or operator outcome, not just a slide or a prototype”
  • Emphasize experience in building AI-native workflows and debugging failures in live environments
    — “You’ve built and owned AI-native workflows or agents in production, and debugged real production failures”
  • Demonstrate your ability to translate vague problems into concrete, measurable technical solutions
    — “can turn a fuzzy problem into a scoped, shipped system”
  • Showcase your ability to integrate systems into customer environments, including auth, data, and tools
    — “integrate them into the customer’s auth, data, workflow, and delivery systems”
  • Include experience with prompt iteration, model evaluation, and system hardening in your resume and interviews
    — “harden it with evals until the team actually uses it”
Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • owned a customer or operator outcome
  • built and owned AI-native workflows in production
  • handled production reliability
Typical background
5+ years of experience in software engineering, machine learning engineering, or data science

Skills & requirements

Required

Software EngineeringMachine Learning EngineeringData ScienceProduction Code (python, Javascript/typescript)Ai-native WorkflowsProduction ReliabilityEnd-to-end Systems

Preferred

Inventive ResearchDesign

Stack & domain

PythonJavaScriptTypeScriptLeadershipCommunicationProblem-solvingSoftware EngineeringMachine LearningData Science

About the role

Original posting from Cursor via Ashby

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code.

ABOUT THE ROLE

You embed directly with customer teams as the engineer on the ground: own an outcome, ship a production-grade SpaceXAI system that still runs after you leave, and become the critical technical feedback loop from the field back to product engineering.

The work is the project. One week you might take a messy business workflow and put a production agent on it: pick the use case, stand up the architecture, wire it into the customer’s auth, data, and tools, then harden it with evals until the team actually uses it. Another week you might live in a customer’s repo and delivery system and ship an agent-powered path for a migration, a PR review loop, or an incident-to-fix flow, behind tests, review, and rollout rather than in a chat sidebar. Either way, you measure what matters, leave the customer able to operate it, and bring the pattern back as another use case for SpaceXAI.

This is not a demo role. You own the work end to end, from the first discovery call through launch, iteration, and post-launch support, and you are responsible for systems that work in the real world.

WHAT YOU’LL DO

  • Lead discovery with the customer: orient in their codebases and workflows, find the real bottleneck and the use case worth building, and define clear success metrics
  • Design and ship production agents, AI applications, and coding-agent workflows on Grok and our models, on live business processes and inside the customer’s engineering org
  • Get a first version live in days, then harden it over weeks with rollout, monitoring, and iteration from real usage
  • Own production quality: tracing, evals, debugging model or delivery failures, and latency and cost tradeoffs
  • Build the systems around the model (tools, MCP servers, retrieval, rules, skills, CI gates, and evals) and integrate them into the customer’s auth, data, workflow, and delivery systems; decide when prompting, architecture, data, process, or a model change is the right lever
  • Measure the outcome that matters (revenue, cost, hours saved, error rate, cycle time, escaped defects), not seat adoption
  • Work directly with Staff+, platform, and domain leaders, going deep in their systems while communicating clearly about tradeoffs and results
  • Leave the customer team able to run what you built, and turn what you learn into reusable patterns and improvements to SpaceXAI products

YOU MAY BE A FIT IF

  • You have 5+ years of experience in software engineering, machine learning engineering, or data science
  • You write and review production code (Python, JavaScript/TypeScript; other languages welcome)
  • You’ve owned a customer or operator outcome, not just a slide or a prototype, and can turn a fuzzy problem into a scoped, shipped system
  • You’ve built and owned AI-native workflows or agents in production, and debugged real production failures (model, tool, data, pipeline, or delivery path)
  • You’ve handled production reliability: metrics, alerts, safe rollouts, incident response
  • You build end to end: frontend, backend, infra, and prompt iteration
  • You thrive in ambiguity and don’t need a complete spec before you start building
  • You can talk to working engineers and to a VP

NICE TO HAVE

  • 2+ years in a customer-facing role, leading discovery conversations and being accountable for outcomes for external stakeholders
  • Hands-on depth in several of: agents and tool-calling, eval harnesses, retrieval and context systems, CI/CD or developer platforms, and AI-native coding workflows in a real repo
  • 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

Source: Cursor careers (Ashby)

Similar roles

More roles at Cursor