Define next-generation SoCs for automotive, robotics, vision, and Physical AI applications, translating real-world workloads into quantitative SoC requirements spanning compute, memory, bandwidth, interconnect, data movement, power, and end-to-end latency.
Locations (3)
San Jose, California, United States
San Diego, California, United States
Austin, Texas, United States
Responsibilities
Translate Physical AI, automotive, robotics, and vision workloads into SoC compute, memory, bandwidth, interconnect, and latency requirements.
Model end-to-end data flows across sensor, CPU, AI accelerators, memory hierarchy, on-chip interconnect, and I/O.
Analyze and optimize data placement and movement across DDR memory, caches, on-chip memory, accelerator-local memory, and buffers.
Correlate AI compute requirements and accelerator performance with memory bandwidth, data movement, utilization, and system-level performance.
Lead Communications and Networking Command and Control (C2) Engineer to serve in a Deputy Trailboss capacity for Army Next Generation Command and Control (NGC2), shaping and delivering interoperable capabilities responsive to operational-unit needs.
Compensation
$147,200 - $184,000 - $220,800 Annual
Responsibilities
Serve as a senior technical integrator and NGC2 Deputy Trailboss, shaping priorities, coordinating execution, and driving delivery of integrated C2 capabilities across acquisition stakeholders, operational units, and industry partners.
Lead end-to-end architectures spanning C2 applications, data layers, tactical IP transport, resilient communications, and cloud & edge compute infrastructure to satisfy Army mission needs, including in disconnected, degraded, intermittent, or limited-bandwidth (DDIL) environments.
Develop and connect operational mission threads, technical threads, system-of-systems designs, requirements, and interface assumptions to measurable operational outcomes and testable technical objectives.
Evaluate cross-layer architecture tradeoffs involving routing, bandwidth, latency, reliability, cybersecurity, interoperability, data exchange, synchronization, and placement of workloads at the edge versus in the cloud.
Organize and participate in laboratory integration, technical demonstrations, operational exercises, and iterative fielding events; coordinate root-cause analysis, integration-risk resolution, and performance assessment across vendor solutions.
Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design.
Responsibilities
Understand business use of data and stakeholder requirements to support work processes and strategic business objectives
Leverage data, software engineering, and data science techniques to create business value through data accessibility. Includes data ingestion, data preparation and analytics processing.
Identify, acquire, cleanse & prepare, store data and develop data products aligned with defined architecture patterns
Responsible for normalizing and ingesting data from multiple sources.
Enable data scientists, making data available for advanced analytics models, and contributing to models.
Develop training and post-training systems for advanced Physical AI models, including world foundation models and robot policies using NVIDIA’s GPU infrastructure.
Responsibilities
Develop and optimize training infrastructure for advanced Physical AI world models, supporting pre-training, supervised fine-tuning (SFT), and reinforcement learning (RL).
Explore distributed parallelism, sharding, low-precision training, compute–communication overlap, and numerical consistency and efficient weight synchronization between training and inference.
Build Physical AI post-training and RL infrastructure supporting advanced training algorithms.
Connect simulation or, where applicable, real-robot interaction with experience collection, rollout inference, reward computation, training, and evaluation.
Optimize these workflows through partitioning, pipelining, data transfer, and synchronization across synchronous, asynchronous, or disaggregated execution.
Internship focused on developing and optimizing AI infrastructure for training, inference, and evaluation of advanced Physical AI models at NVIDIA’s Cosmos Lab.
Responsibilities
Develop and optimize training infrastructure for advanced Physical AI world models
Build Physical AI post‑training and RL infrastructure
Improve efficiency and scalability across training, inference, simulation, and evaluation
Analyze and optimize system performance with researchers
Perform what-if and scaling analyses across camera count, resolution, frame rate, AI model characteristics, precision, memory configuration, and accelerator performance.
Requirements
BS, MS, or PhD in Electrical Engineering, Computer Engineering, Computer Science, or a related field
Intern, AI System Architecture Research ScientistNewSamsung Semiconductor · San Jose, US
Software Engineer - Data PlatformsNewQube Research & Technologies · Hong Kong, Hong Kong
Physical AI SoC ArchitectAdvanced Micro Devices, Inc · San Jose, US+2 more
AI Solution Architecture DirectorSK hynix America · San Jose, US
Lead Comms & Networking C2 EngineerThe Mitre Corporation · Aberdeen, US