The role
Siemens builds the systems the physical world runs on: factories, power grids, buildings, trains, hospitals. Industrial and physical AI is a major opportunity in applied AI, and one of the harder ones to get right. There is a generation of AI-powered products to build.
We are building systems that integrate machine learning models into real world, production grade engineering platforms. These systems operate at the intersection of applied science, distributed systems, and real time product delivery.
As Director of Engineering (Pod Lead), you own the technology execution and delivery required to take these systems from concept into reliable, deployed production environments. You are accountable for ensuring that engineering systems ship with applied science models embedded in them, and continue to operate at scale with high quality, reliability, and velocity.
This is a senior engineering leadership role. You will lead a cross-functional engineering pod and serve as the single accountable owner for delivery outcomes, partnering closely with Product Management and Applied Science while remaining personally responsible for execution. The role requires both strategic judgment and operational rigor: someone who can make delivery tradeoffs, lead teams through ambiguity, and stay close enough to the work to ensure the right systems get built and deployed.
Key responsibilities
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Own end-to-end delivery of AI-powered systems, from planning through deployment, operation, and iteration.
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Lead and scale an engineering pod responsible for building and operating production systems that embed ML models.
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Partner with Product Management to convert product strategy into executable delivery plans.
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Partner with Applied Science to take models from research into production-grade systems, effectively and safely.
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Ensure engineering systems meet the standards for reliability, scalability, observability, and operational excellence.
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Make delivery tradeoffs across scope, schedule, quality, and technical risk.
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Drive execution across infrastructure, product engineering, and ML-enabled systems.
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Hire, develop, and retain engineering managers and senior engineers.
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Establish delivery discipline across planning, code quality, release readiness, and incident response.
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Hold the pod accountable for outcomes.
Basic qualifications
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12+ years of professional software engineering experience
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5+ years leading multi-team engineering organizations at Senior EM or Director level.
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Demonstrated ownership of shipping and operating complex distributed systems in production.
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Experience delivering systems that incorporate machine learning or AI components.
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Strong system-level understanding across software architecture, ML pipelines, and production operations.
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Proven ability to lead teams through technical and organizational ambiguity.
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Track record of balancing delivery velocity with long-term system health.
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Demonstrated ability to recruit and retain senior engineers and engineering managers in a competitive market.
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Clear, decisive communicator with credibility across engineering, product, and senior leadership.
Preferred qualifications
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Experience leading AI or ML-powered product platforms end to end.
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Background in industrial, real-time, or safety-critical systems.
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Familiarity with modern AI systems in production: LLMs, agents, generative AI, retrieval, model serving, inference optimization, MLOps.
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Experience scaling teams and delivery practices in growing organizations.
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Comfortable operating in global, cross-functional environments.
