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Archer Technologies is building an all-electric vertical takeoff and landing aircraft for sustainable air mobility. As Director of AI-Native Engineering Platform & Tools, you will lead the cross-domain platform that enables Archer's flight software, autonomy, and verification teams to ship certified, airworthy software with modern velocity and aviation-grade safety.
You own the engineering platform engine: AI-assisted development and verification, MLOps and simulation infrastructure, code-to-flight CI/CD, developer tooling, observability, and DevOps/SRE foundations. Your customers are Archer's engineers; success is measured by how much faster and more reliably the entire organization ships.
Key responsibilities include deploying agentic AI and LLM tooling across the software development lifecycle (code generation, test generation, requirements traceability, documentation, automated verification), building internal AI tools and "paved roads" that become the default way engineers work, and operating MLOps and simulation infrastructure for AI/ML and autonomy with reliability and traceability. You will own the end-to-end engineering platform including build systems, CI/CD, test frameworks, and deployment pipelines. You'll make "fast" and "certifiable" the same path by automating evidence and traceability, and standing up DO-330 tool qualification for certified software. You'll integrate security gates from Product Cybersecurity into the platform so the paved road is secure by default.
This is a hands-on leadership role at the intersection of AI, autonomy, infrastructure, and engineering operations. You will lead the vision and roadmap for the AI-native platform, mentor and grow a high-performing team spanning AI Platform Tools, Core OS, and DevOps/SRE, and partner closely with Flight Software, Autonomy/ML, Systems Engineering, and Product Cybersecurity.
Required: 10+ years in software engineering with significant depth in platform/infrastructure, developer tooling, or AI/ML systems. Proven hands-on technical leadership with experience managing engineers and scaling teams. Deep expertise building tools, platforms, and infrastructure for software delivery and AI/ML development. Practical experience applying modern AI to engineering (agentic/LLM tooling, MLOps, AI-assisted development). Strong command of distributed systems, cloud infrastructure, infrastructure-as-code, CI/CD, observability, and developer-experience tooling.
Preferred: Experience integrating AI/ML into real-time, safety-critical, or regulated systems. Exposure to aerospace, robotics, autonomy, automotive, or other safety-critical domains.
