Senior Level Robotics AI Architect

WHAT YOU DO AT AMD CHANGES EVERYTHING 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. THE ROLE: We are seeking a Robotics AI Architect to define and scale next-generation Physical AI systems, with a focus on complex robotic platforms (including humanoids). This role emphasizes architectural leadership across multi-layer AI control loops and tight collaboration with lighthouse customers to achieve production-grade performance targets. THE PERSON: As a key technical authority, you will synthesize learnings from real-world deployments and translate them into platform-defining capabilities, shaping the roadmap for our AI SDKs, runtime, and reference architectures to enable broad ecosystem scalability. KEY RESPONSIBILITIES: End-to-End AI Control Loop Architecture (Core Focus) Define architecture for hierarchical AI control loops, including: Perception (sensor fusion, VLMs, state estimation) World modeling and scene understanding Task and motion planning Low-latency control and actuation Learning and adaptation loops Establish timing models and system-level constraints: Deterministic execution for control loops (µs–ms) Bounded latency for perception/planning (ms-scale) Guide architectural decisions to: Minimize latency, jitter, and data movement Optimize compute utilization across heterogeneous systems Influence compute-software co-design across CPU, GPU, and accelerators Lighthouse Customer Co‑Engineering Act as architectural lead for strategic/lighthouse customers, guiding: System design decisions Performance trade-offs Deployment architecture Influence customer implementations to align with: Platform best practices Scalable architectural patterns Translate real-world constraints (latency, power, safety) into: Platform requirements Architecture refinements Lead deep technical engagements, including: Architecture and design reviews Performance tuning strategies System-level debugging approaches Influencing Robotics Reference Architectures, Platform Architecture & SDKs Define reference architecture for complex robotic systems (humanoids, high-DoF manipulators, mobile manipulation platforms), establishing industry-leading blueprints for Physical AI systems Influence architectural partitioning strategies across: On-robot compute (real-time loops) Edge/accelerator subsystems Cloud (training, simulation, fleet learning) Provide architectural guidance on: Whole-body control integration Locomotion and balance systems Dexterous manipulation pipelines Multi-modal perception stacks Serve as a bridge between lighthouse deployments and platform evolution, translating system-level insights into: SDK feature direction Runtime and middleware enhancements Reference pipeline abstractions Shape the roadmap of: Physical AI SDK and runtime frameworks Robotics middleware integrations (ROS2 and beyond) Dataflow and scheduling architectures for deterministic execution Identify systemic gaps and influence solutions in: Scheduling and orchestration models Memory and dataflow efficiency Inter-process/inter-node communication Real-time guarantees and QoS mechanisms Drive the creation of scalable architectural patterns, including: Reusable operator graphs and pipelines Standardized deployment topologies Benchmark and validation frameworks Ensure that lighthouse customer learnings are abstracted and generalized into: Repeatable reference architectures Platform capabilities consumable across a wide customer base PREFERRED EXPERIENCE: 17+ years of experience in Robotics, Autonomous systems AI architecture, definition and development, RL work, Sim-to-real, cloud-to-sim, real-to-sim/cloud from AI perspective. Proven technical leadership experience influencing external and internal stakeholders Deep understanding of: AI inference runtimes and deployment tradeoffs System architecture level CPU/GPU/NPU scheduling and contention System‑level performance, latency, and isolation Software frameworks and usage (multimedia, ROS2, OpenCV, gstreamer etc.) Industry leading SW inference frameworks (vLLM etc.), runtimes, tools Performance bottleneck, characterization Determinism, real-time and safety considerations in mixed-criticality systems Ability to engage credibly with customer’s engineering leaders, AI architects Track record of transforming customer deployments into platform and roadmap feedback Hands-on architects who can guide engineers, debug problems, create innovative PoCs as well as abstract unnecessary details for executive presentations Familiarity/experience with AMD GPU and NPU AI SW stacks and tools will be a plus ACADEMIC CREDENTIALS: Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field #LI-MH2 This role is not eligible for visa sponsorship. Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

THE ROLE: We are seeking a Robotics AI Architect to define and scale next-generation Physical AI systems, with a focus on complex robotic platforms (including humanoids). This role emphasizes architectural leadership across multi-layer AI control loops and tight collaboration with lighthouse customers to achieve production-grade performance targets. THE PERSON: As a key technical authority, you will synthesize learnings from real-world deployments and translate them into platform-defining capabilities, shaping the roadmap for our AI SDKs, runtime, and reference architectures to enable broad ecosystem scalability. KEY RESPONSIBILITIES: End-to-End AI Control Loop Architecture (Core Focus) Define architecture for hierarchical AI control loops, including: Perception (sensor fusion, VLMs, state estimation) World modeling and scene understanding Task and motion planning Low-latency control and actuation Learning and adaptation loops Establish timing models and system-level constraints: Deterministic execution for control loops (µs–ms) Bounded latency for perception/planning (ms-scale) Guide architectural decisions to: Minimize latency, jitter, and data movement Optimize compute utilization across heterogeneous systems Influence compute-software co-design across CPU, GPU, and accelerators Lighthouse Customer Co‑Engineering Act as architectural lead for strategic/lighthouse customers, guiding: System design decisions Performance trade-offs Deployment architecture Influence customer implementations to align with: Platform best practices Scalable architectural patterns Translate real-world constraints (latency, power, safety) into: Platform requirements Architecture refinements Lead deep technical engagements, including: Architecture and design reviews Performance tuning strategies System-level debugging approaches Influencing Robotics Reference Architectures, Platform Architecture & SDKs Define reference architecture for complex robotic systems (humanoids, high-DoF manipulators, mobile manipulation platforms), establishing industry-leading blueprints for Physical AI systems Influence architectural partitioning strategies across: On-robot compute (real-time loops) Edge/accelerator subsystems Cloud (training, simulation, fleet learning) Provide architectural guidance on: Whole-body control integration Locomotion and balance systems Dexterous manipulation pipelines Multi-modal perception stacks Serve as a bridge between lighthouse deployments and platform evolution, translating system-level insights into: SDK feature direction Runtime and middleware enhancements Reference pipeline abstractions Shape the roadmap of: Physical AI SDK and runtime frameworks Robotics middleware integrations (ROS2 and beyond) Dataflow and scheduling architectures for deterministic execution Identify systemic gaps and influence solutions in: Scheduling and orchestration models Memory and dataflow efficiency Inter-process/inter-node communication Real-time guarantees and QoS mechanisms Drive the creation of scalable architectural patterns, including: Reusable operator graphs and pipelines Standardized deployment topologies Benchmark and validation frameworks Ensure that lighthouse customer learnings are abstracted and generalized into: Repeatable reference architectures Platform capabilities consumable across a wide customer base PREFERRED EXPERIENCE: 17+ years of experience in Robotics, Autonomous systems AI architecture, definition and development, RL work, Sim-to-real, cloud-to-sim, real-to-sim/cloud from AI perspective. Proven technical leadership experience influencing external and internal stakeholders Deep understanding of: AI inference runtimes and deployment tradeoffs System architecture level CPU/GPU/NPU scheduling and contention System‑level performance, latency, and isolation Software frameworks and usage (multimedia, ROS2, OpenCV, gstreamer etc.) Industry leading SW inference frameworks (vLLM etc.), runtimes, tools Performance bottleneck, characterization Determinism, real-time and safety considerations in mixed-criticality systems Ability to engage credibly with customer’s engineering leaders, AI architects Track record of transforming customer deployments into platform and roadmap feedback Hands-on architects who can guide engineers, debug problems, create innovative PoCs as well as abstract unnecessary details for executive presentations Familiarity/experience with AMD GPU and NPU AI SW stacks and tools will be a plus ACADEMIC CREDENTIALS: Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field #LI-MH2 This role is not eligible for visa sponsorship.