Forward Deployed Engineer (Australia)

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.

SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.

About the role

You embed directly with customer teams as the engineer on the ground: own an outcome, ship a production-grade SpaceXSI 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 SpaceXSI.

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 SpaceXSI products

You may be a fit if

Required

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