Director, Software Engineering

Job description

Job responsibilities

• Set the technical vision and multi-year roadmap for a new Applied AI platform to simplify complex, multi-step customer workflows across Cognex’s product portfolio.
• Direct applied R&D on emerging AI capabilities, with structured experimentation and prototyping and a clear path from research to production.
• Lead architecture decisions spanning embedded devices, edge compute, and cloud services and infrastructure, ensuring seamless experiences across hybrid experiences.
• Own the reliability, scalability, security, cost efficiency and operational excellences of the services that power the platform, including APIs, model-serving infrastructure, data pipelines, and connected experiences.
• Establish the engineering standards, evaluation frameworks, guardrails, observability, and operational practices (SLAs, on-call, incident response) required to deliver trustworthy AI systems in production, balancing…
• Partner with Product, R&D, UX, and Hardware organizations to turn ambiguous problems into shipped capabilities, on committed timelines.
• Drive adoption of the platform across Cognex's product portfolio, enabling product teams to rapidly build and consume shared AI capabilities and services.
• Track the fast-moving AI landscape and translate external advances into pragmatic bets for Cognex.

Job requirements

• 15+ years of software engineering experience, including 5+ years in senior engineering leadership roles managing managers and/or multiple engineering teams, with a proven track record of building high-performing…
• Track record of shipping and scaling complex platforms and cloud/online services in production.
• Experience leading R&D or applied-research efforts from concept to shipped product
• Demonstrated experience turning ambiguous customer problems into shipped products, scalable products and technical strategy
• Deep expertise in backend/cloud architecture, API design and platform engineering.
• Hands-on familiarity with modern AI application architectures, including LLMOps, agentic systems, tool integration, retrieval augmented generation (RAG), Model Context Protocol, and evaluation harness design, prompt…
• Strong strategic and execution-oriented mindset, with the ability to balance ambitious AI innovation against reliability, scalability, cost, and deployment constraints.
• Exceptional communication, influencing, and stakeholder management skills, with the ability to align technical teams, executives, customers, and cross-functional partners around a shared vision and execution plan.