Internal AI Job Board

Director, AI-Native Engineering Platform & Tools

Archer - San Jose, California, United States

This role is part of the Internal AI Job Board, a curated list of jobs for teams building or operating internal AI systems. Follow the original role page for the employer description, requirements, and application flow.

Compensation: base pay between $250,000 - $310,000. Actual compensation offered will be determined by factors such as job-related k

Responsibilities

• Archer's software organization is built on a conviction: AI-native engineering will let us ship certified, airworthy software with the velocity of modern technology and the integrity of aviation safety — and out-build anyone in this industry.
• This leader owns the engine that makes that true. You will build the cross-domain platform — "by machines, for machines" — that every flight software, GNC, autonomy, and V&V team runs on: AI-assisted development and verification, MLOps and simulation infrastructure, code-to-flight CI/CD, developer tooling and internal "paved roads," observability, and the DevOps/SRE and Core OS foundations underneath. Your customers are Archer's engineers; your product is their velocity and quality.
• This is a hands-on leadership role at the intersection of AI, autonomy, infrastructure, and engineering operations. You will partner closely with Flight Software & Algorithms, Autonomy/ML, Systems Engineering, and Product Cybersecurity — and you will be measured by how much faster, and how much more reliably, the entire organization ships.
• AI-Native Engineering (the core of the role)
• Make Archer's engineers AI-native: deploy agentic AI and LLM tooling across the SDLC — code generation and review, test generation, requirements-to-code-to-evidence traceability, documentation, and automated verification.
• Build internal AI tools and "paved roads" that compound developer velocity and become the default way Archer engineers work.
• Build and operate the MLOps and simulation infrastructure for AI/ML and autonomy: data processing, model training, evaluation, validation, and deployment — with reliability, traceability, and performance across the full lifecycle from research to production.
• Create the harnesses that integrate AI and autonomy components into flight-critical software reliably and verifiably.
• Own the end-to-end engineering platform: build systems, CI/CD, test and verification frameworks, deployment pipelines, and the path from code to flight.
• Build developer tooling and observability that measurably improve productivity, reliability, and operational efficiency.
• Lead the DevOps/SRE and Core OS foundations that the rest of the org depends on.
• Make "fast" and "certifiable" the same path: automate evidence, traceability, and assurance, and stand up DO-330 tool qualification for the platform and tools that touch certified software.
• Integrate the security gates and policies owned by Product Cybersecurity into the platform so the paved road is secure by default — security is a partnership here, not a function this role staffs or owns.
• Lead the vision, roadmap, and execution for the AI-native engineering platform; mentor and grow a high-performing team spanning AI Platform Tools & Processes, Core OS, and DevOps/SRE.
• Partner with Flight Software, Autonomy/ML, Infrastructure, and Systems Engineering to define requirements and deliver scalable solutions.
• Communicate complex technical concepts clearly, align technical decisions with aircraft development milestones, debate on merit, and commit fully.

Qualifications

• 10+ years in software engineering, with significant depth in platform/infrastructure, developer tooling, or AI/ML systems.
• Proven hands-on technical leadership; experience managing engineers and/or managers and scaling teams.
• Deep expertise building tools, platforms, and infrastructure for software delivery and for AI/ML development and deployment.
• Practical experience applying modern AI to engineering — agentic/LLM tooling, MLOps, or AI-assisted development.
• Strong command of distributed systems, cloud infrastructure, infrastructure-as-code, CI/CD, observability, and developer-experience tooling.
• Experience integrating AI/ML into real-time, safety-critical, or regulated systems.
• Exposure to aerospace, robotics, autonomy, automotive, or other safety-critical domains.
• Experience standing up high-velocity engineering organizations and internal developer platforms (ref: Team Topologies).
• Familiarity with DO-178C / DO-330 tool qualification or other certification regimes.

Apply on the original posting