THE ROLE We are seeking a senior engineering leader for the AI System Performance, RAs Customer Co-Engineering and System Deployment to lead deep, technical customer engagements that enable successful deployment of AI on edge and physical AI platforms at the system level.
THE PERSONThis role owns the end-to-end lighthouse customer AI deployment journey-from early single-model evaluation, experience and performance optimization, through multi-model, concurrent, multi-tenant system deployment, integrated with market-specific workloads such as robotics control, digital cockpit graphics, real-time perception, and safety-critical systems. Taking those learning from these strategic customer deployments to create right reference architectures demonstrating most important system performance metrics to scale the business.
You will lead and grow a senior, multidisciplinary co-engineering organization that bridges AI models, toolchains and runtimes, system-software and segment SDKs, sensor and media pipelines, and silicon, ensuring light-house customers adopt AMD solutions and execute smoothly from proof-of-concept to scalable, production-grade systems.
This is a technical leadership role, focused on critical reference architectures enabling real scalable deployments-not just demos or POCs. deploying multiple models, concurrently, at scale, within real embedded systems is challenging problem. This role ensures AI succeeds in real products, under real constraints, across robotics, digital cockpit, and physical AI deployments.
KEY RESPONSIBILITIES: Customer AI Deployment Leadership- Own senior-level technical relationships with strategic AI customers across Robotics, Digital Cockpit, Industrial, Automotive, and Edge/Physical AI markets
- Serve as the senior technical authority guiding customers through the full AI deployment lifecycle:
- Single-model bring-up and correctness validation
- Model-level performance profiling and optimization
- Runtime selection, graph partitioning, and acceleration
- System-level optimization across multiple simultaneous AI models
- Multi-tenant execution alongside domain workloads (control, vision, graphics, audio, safety)
- Agentic AI workloads and optimization
- Act as the technical escalation point for system-level issues involving latency, determinism, contention, isolation, and stability
AI Deployment Journey Ownership- Define and standardize the AI deployment journey for customers:
- Model evaluation (accuracy, latency, resource footprint)
- Single-model optimization (CPU/GPU/accelerator efficiency)
- Multi-model concurrency (pipelines running simultaneously)
- Multi-tenancy and isolation across workloads and applications
- System-level tuning under real operating conditions
- Guide customers in transitioning from model-centric thinking to system-centric deployment
- Ensure AI workloads coexist predictably with:
- Robotics control loops and real-time pipelines
- Digital cockpit graphics, vision, and HMI stacks
- Safety-critical, deterministic, or long-lifecycle software
- Building mission-critical AI reference architectures for easy and scalable deployment of AI across the large spectrum embedded and physical AI Customers.
Market-Specific System Integration- Oversee AI system deployments across multiple edge/physical AI domains, including:
- Robotics & Autonomy: perception, planning, sensor fusion, control, safety
- Digital Cockpit: vision-AI, driver monitoring, in-cabin AI, graphics coexistence
- Industrial / Physical AI: inspection, monitoring, closed-loop automation, autonomous agents
- Create key AI reference architectures as relevant to broad set of markets
- Ensure AI deployment strategies respect domain-specific constraints:
- Real-time guarantees and determinism
- Graphics + AI coexistence (especially in cockpit)
- Safety, isolation, and mixed-criticality requirements
- Power, thermal, and resource budgeting
Organizational Leadership- Build, scale, and mentor a high-performing team with expertise in segments,
- Define roles across:
- AI inference and runtime optimization
- System software and scheduling
- Domain-specific AI deployment (robotics, cockpit, industrial)
- Establish consistent engagement models for:
- Early silicon and platform access
- AI deployment readiness assessments
- Prototype-to-production transitions
- Ensure customer learning is captured, generalized, and reused
- Work internally with SDK and Core AI engineering teams to bring leading edge features into the customer front.
Platform, SDKs, and Roadmap Influence- Serve as the key conduit from real deployments to internal roadmap and development strategy
- Translate customer AI deployment challenges into:
- SDK and runtime requirements
- Tooling, profiling, and observability improvements
- Platform and silicon architecture feedback
- Influence platform priorities to support:
- Multi-model concurrency
- Efficient scheduling and isolation
- Long-lifecycle, mixed-criticality deployments
- Scalable AI deployment patterns across market segments
Cross-Functional Leadership- Partner closely with:
- Platform architecture, SDKs, Core AI engineering and product management
- Silicon and system software engineering
- Ecosystem and strategic partnerships
- Align internal teams around a deployment-first view of AI success, not benchmark-driven optimization only
- Define clear boundaries and handoffs between:
- Customer co-engineering
- Core AI and SDK teams
- Partner and ecosystem enablement
PREFERRED EXPERIENCE:- 17+ years of experience in AI systems performance and deployment in the markets of edge computing, robotics, embedded platforms, or physical AI
- Proven leadership experience managing senior technical teams
- Deep understanding of:
- AI inference runtimes and deployment tradeoffs
- CPU/GPU/NPU scheduling and contention
- System-level performance, latency, and isolation
- Software frameworks and usage (multimedia, ROS2, OpenCV, gstreamer etc.)
- Industry leading inference frameworks (vLLM etc.)
- Strong background in:
- Embedded Linux and/or RTOS environments
- Multi-process, multi-tenant system design
- Edge AI deployment at scale
- Runtimes and Offline tools
- Ability to engage credibly with customer's engineering leaders, AI architects and leaders
- Track record of transforming customer deployments into platform and roadmap feedback
- Hands-on leader who can get into technical details with the engineers as well as uplevel the complexity to the executives
Preferred / Differentiators- Experience deploying AI in real-time or safety-sensitive environments
- Background in robotics, autonomous systems, embedded/edge or digital cockpit platforms
- Experience with mixed-criticality systems (QNX, AUTOSAR Adaptive, real-time Linux)
- History of influencing multi-generation platform or silicon roadmaps
- Experience moving customers from PoC to volume production
- Experience with AMD NPU and GPU AI SW stacks and tools will be even better
PREFERRED LOCATION:- San Jose / Santa Clara, CA or Austin, TX
This role is not eligible for visa sponsorship.#LI-MH2 Benefits offered are described: AMD benefits at a glance.
