Director Software System Design / AI System Performance

Advanced Micro Devices, Inc•San Jose, CA

7d•Hybrid

About The Position

At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. 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. This 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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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.
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
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.
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
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