Forward Deployment Application Engineer

Intuitive Machines; Houston (TX); In loco; USD 150.000 - 210.000; Tempo pieno

Descrizione del lavoro

Intuitive Machines in Houston, TX, is building and deploying AI applications to improve spacecraft engineering, manufacturing, and services. As a member of the AI Strike Team, you will own end‑to‑end delivery from discovery to deployment, working with engineers and business users to turn prioritized problems into useful software with measurable impact.

Responsibilities include embedding with users, building interfaces and APIs, integrating enterprise systems, implementing retrieval‑augmented

Competenze

• Must be a US Citizen or permanent resident.
• Bachelor’s degree; four additional years of experience may substitute for a degree.
• Minimum of 15 years of software, application, solutions, or data engineering experience, with evidence of shipping and supporting software used by real customers.
• Strong Python and API‑development skills; experience building usable application interfaces in TypeScript, JavaScript, or a comparable framework.
• Hands‑on experience delivering LLM applications, including retrieval, structured outputs, tool integration, and evaluation.
• Experience integrating APIs, databases, documents, and enterprise systems; practical understanding of authentication, authorization, data modeling, and data quality.
• Proficiency with Git, automated testing, CI/CD, containers, and cloud deployment.
• Ability to lead technical discovery, explain tradeoffs clearly, work through incomplete requirements, and deliver a bounded solution within a short sprint.
• Sound judgment in handling sensitive information, maintaining traceability, and designing workflows with accountable human review.

Mansioni

• Embed with users to observe actual work, map handoffs and bottlenecks, establish baselines, and define a feasible MVP with the Product Lead and domain owner.
• Build application interfaces, backend services, APIs, and workflow automation. Put working prototypes in front of users early and improve them through observed use.
• Integrate enterprise and engineering systems through reusable connectors, APIs, and data transformations. Preserve source permissions, revisions, configuration, units, and provenance.
• Implement retrieval‑augmented generation, structured outputs, and tool calling with grounded answers, citations, and explicit human approval where needed.
• Create representative evaluation cases and regression tests. Measure task completion, retrieval accuracy, review effort, latency, reliability, and operating cost.
• Deploy through approved GitLab CI/CD and DevSecOps patterns. Implement monitoring, error handling, access controls, recovery, and documentation for sustainment.

Conoscenze

Python

API development

TypeScript/JavaScript

LLM applications

Retrieval-augmented generation

CI/CD & DevSecOps

Cloud deployment

Data engineering

Formazione

Bachelor’s degree

Strumenti

AWS GovCloud

Bedrock

GitLab

Descrizione del lavoro

About Intuitive Machines

Intuitive Machines is an innovative and cutting‑edge space company making cislunar space accessible to both public and private customers. Our mission is to further science and exploration, communications, and economic progress from the Earth to the Moon and beyond. With multiple NASA lunar missions in development and additional private missions on our manifest, we pride ourselves in supporting NASA, our customers, and the nation in paving the way to return humans to the surface of the Moon. Our world‑class team includes experts in all aspects of spacecraft subsystems design, development, and test, on‑orbit operations, and safety.

Houston, TX

About The Position

Build and deploy AI applications that improve spacecraft engineering, manufacturing, and services. As a member of the AI Strike Team, you will work directly with engineers and business users to turn prioritized problems into useful software through six‑week delivery sprints. You will own application delivery from discovery and prototyping through integration, evaluation, deployment, and technical handoff. Success means measurable improvements in cycle time, engineering effort, quality, or program execution, supported by reliable applications used in real workflows.

What You Will Build

The initial focus is five foundations: self‑service access to approved data for LLM use; connectors for users who need help accessing data; use‑case‑specific subsystem assistants; curated datasets requiring extraction, transformation, and loading; and flight software development tools operating within an approved GovCloud AI environment. Evolve proven workflows into bounded agents as their reliability and value justify it.

Responsibilities

• Embed with users to observe actual work, map handoffs and bottlenecks, establish baselines, and define a feasible MVP with the Product Lead and domain owner.


• Build application interfaces, backend services, APIs, and workflow automation. Put working prototypes in front of users early and improve them through observed use.


• Integrate enterprise and engineering systems through reusable connectors, APIs, and data transformations. Preserve source permissions, revisions, configuration, units, and provenance.


• Implement retrieval‑augmented generation, structured outputs, and tool calling with grounded answers, citations, and explicit human approval where needed.


• Create representative evaluation cases and regression tests. Measure task completion, retrieval accuracy, review effort, latency, reliability, and operating cost.


• Deploy through approved GitLab CI/CD and DevSecOps patterns. Implement monitoring, error handling, access controls, recovery, and documentation for sustainment.

Requirements

• Must be a US Citizen or permanent resident


• Bachelor’s degree, four additional years of experience may be substituted for a degree


• Minimum of 15 years of software, application, solutions, or data engineering experience, with evidence of shipping and supporting software used by real customers.


• Strong Python and API‑development skills; experience building usable application interfaces in TypeScript, JavaScript, or a comparable framework.


• Hands‑on experience delivering LLM applications, including retrieval, structured outputs, tool integration, and evaluation. Ability to distinguish model limitations from data, workflow, and software defects.


• Experience integrating APIs, databases, documents, and enterprise systems; practical understanding of authentication, authorization, data modeling, and data quality.


• Proficiency with Git, automated testing, CI/CD, containers, and cloud deployment. Ability to debug across application, integration, and infrastructure boundaries.


• Ability to lead technical discovery, explain tradeoffs clearly, work through incomplete requirements, and deliver a bounded solution within a short sprint.


• Sound judgment in handling sensitive information, maintaining traceability, and designing workflows with accountable human review.

Preferred Experience

• Aerospace, defense, manufacturing, or another regulated engineering environment; familiarity with configuration control, quality processes, and engineering verification.


• AWS GovCloud and Bedrock, enterprise model gateways, Open WebUI, MCP interfaces, and secure development practices for CUI and ITAR workloads.


• Integrations with PLM, ERP, MES, or ALM systems such as Teamcenter, IFS, Solumina, Jama, or GitLab; exposure to Nastran or other engineering analysis tools.

How You Will Work With The Team

The Product Lead sets priorities and business outcomes, with AI Council oversight. You provide feasibility estimates and own application implementation. The Core Platform and DevSecOps team supplies approved infrastructure and shared services. Domain engineers validate technical outputs and retain approval authority. The AI Enablement Manager scales training and adoption; you provide demonstrations, technical guidance, and usage instrumentation.

What Success Looks Like

• Within 30 days, understand a priority workflow, establish its baseline, confirm data and environment readiness, and demonstrate a useful prototype.


• By the end of the first six‑week sprint, deliver an evaluated pilot to the intended users, document results and limitations, and support a scale, iterate, or stop decision.


• Within 90 days, move a successful pilot toward supported production, demonstrate measured improvement including human review effort, and contribute reusable components for subsequent sprints.

US EEO Statement

Intuitive Machines is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by law.