Lead AI Enablement Engineer, Aerospace Programs

About the role:

Archer is building the Midnight aircraft on an aggressive program timeline, and our aerospace engineering teams — structures, GN&C, propulsion, avionics, flight test, certification — are where the schedule is won or lost. A lot of the work that gates those teams is still manual: stitching data between tools, writing one-off scripts that take a week, generating reports by hand, waiting on someone else to build a piece of internal tooling that should have taken an afternoon.

This role exists to change that. You will own AI tooling adoption across the aerospace engineering org as a senior individual contributor — setting the strategy, building the playbook, shipping the early proofs yourself, and steadily levering the org until AI-assisted workflows are a default part of how every engineer works. Think Claude, Claude Code, Cursor, agentic workflows, custom internal tools built on LLMs. You are not an AI researcher. You are a senior aerospace engineer who has become genuinely obsessed with what these tools can do, and whose job is to compress program timelines by getting that leverage into every team that needs it.

This is a senior staff / principal-level role. You'll operate with significant autonomy, set direction for an area the company is investing in heavily, and be expected to make the case to engineering leadership about where to push next. You will not manage people directly, but your influence over how the aerospace org works will be substantial.

Success looks like an aerospace engineering org where AI-assisted workflows are unremarkable — where the analyst who used to spend three days on a post-processing pipeline now ships it in an afternoon, where the test engineer who can't code is shipping their own tools, and where weeks of calendar time have come out of the critical path because of work you enabled.

What you'll do:

• Own the strategy for AI tooling adoption across aerospace engineering. Decide where the leverage is, build the multi-quarter roadmap, and make the case to engineering leadership for where to invest.
• Embed with aerospace teams (structures, GN&C, propulsion, avionics, flight test, certification, manufacturing engineering) to find the workflows where AI tooling unlocks the biggest gains. Sit with engineers. Watch what they do. Find the slow parts.
• Build the first version yourself. When a team has a manual process that's been "the way we do it" for years, write the prototype tool that replaces it — usually in days, not quarters. Demonstrate what's possible, then hand it off or productize it.
• Run enablement at every level — onboarding sessions, office hours, async docs, 1:1 coaching with senior engineers and directors. Teach engineers how to think with these tools, not just type commands into them.
• Mentor and force-multiply. Identify AI-curious engineers inside aerospace teams, equip them as local champions, and turn them into the leverage that lets you scale beyond what one person could touch directly.
• Maintain the internal toolkit: prompt libraries, starter templates, internal MCP servers, and reference implementations tailored to aerospace engineering workflows.
• Partner with IT, security, and the export control team to make sure everything you ship is compliant with ITAR, data handling, and IP requirements.
• Track adoption and impact rigorously. Cycle-time savings, tools shipped, engineers onboarded, workflows automated. Report the numbers up to engineering leadership and use them to prioritize the next bet.
• Stay on the frontier. Try every new model, agent, IDE integration, and harness as it comes out. Be the person — for the whole company — who knows what's actually possible this quarter, not last year.

What you'll bring:

Required:

• BS or higher in mechanical engineering, aerospace engineering, or an equivalent physical-systems discipline. You need to understand the actual work — loads analysis, test data, control systems, CAD/CAE pipelines — because credibility with senior aerospace engineers is non-negotiable.
• 8+ years working in an engineering role on physical systems (aerospace, defense, automotive, robotics, advanced manufacturing), with a track record of impact that justifies a senior staff / principal-level hire. Years matter less than the slope; we're hiring for the bar, not the tenure.
• A genuine, sustained obsession with AI coding and agentic tools. You use Claude Code, Cursor, or equivalent every day. You've shipped non-trivial things with them. You have informed, opinionated views on what works, what doesn't, and where the frontier is moving — and you can back those views up with what you've built.
• Demonstrated ability to drive change across an engineering org without direct authority. You've been the person who got senior, skeptical engineers to adopt a new way of working, and you can point to specific examples.
• Strong programming fundamentals — Python at minimum. You don't have to be a software engineer, but you have to be able to build real, useful things end-to-end and ship them to other engineers.
• Excellent written and verbal communication. You can pitch a strategy to engineering leadership in the morning and coach a structural analyst through their first agentic workflow in the afternoon.

Strongly preferred:

• Experience in eVTOL, aviation, aerospace, or another safety-critical / regulated industry.
• Fluency with engineering tooling that aerospace teams actually use: MATLAB, the Python scientific stack, CAD/CAE (NX, ANSYS, Abaqus, etc.), test data systems, simulation pipelines.
• A public footprint that reflects builder energy — shipped side projects, open-source contributions, writing, talks, or other evidence of pushing on the frontier of these tools outside of any job.
• Hands-on experience with MCP, agent frameworks, evals, or building and maintaining internal LLM-powered tools at scale.
• Prior experience as the senior-most technical voice in a domain — staff/principal at a previous company, or equivalent ownership.

How you'll be measured:

This role has a single overriding metric: did the aerospace program move faster because of you?

In practice that breaks down into:

• Adoption depth and breadth of AI tooling across aerospace engineering teams.
• Number and impact of legacy/manual workflows replaced or accelerated.
• Estimated cycle-time savings on program-critical workstreams, with specific milestone contributions identified.
• Quality of the internal toolkit you build and maintain — measured by reuse, not just shipping.
• Sentiment from the engineers you serve. They should be asking for more of your time, not avoiding it.
• Influence on the broader engineering strategy — your point of view should shape how leadership thinks about AI adoption, not just execute against it.

At Archer we aim to attract, retain, and motivate talent that possess the skills and leadership necessary to grow our business. We drive a pay-for-performance culture and reward performance that supports the Company’s business strategy. For this position we are targeting a base pay between $228,000 - $285,000. Actual compensation offered will be determined by factors such as job-related knowledge, skills, and experience.

Archer is committed to working with and providing reasonable accommodations to job applicants with physical or mental disabilities, and those with sincerely held religious beliefs. Applicants who may require reasonable accommodation for any part of the application or hiring process should provide their name and contact information to Archer’s People Team at [email protected]. Reasonable accommodations will be determined on a case-by-case basis.