Systems Engineer (Dallas)

Overview : We are seeking a Hardware-Focused AI Systems Engineer to lead the integration of machine learning models with custom hardware systems. In this role, you will focus on the design, optimization, and deployment of AI systems across various hardware platforms including GPUs, TPUs, FPGAs, ASICs, and embedded systems for real-time applications in industries like autonomous vehicles, robotics, and IoT. You will work closely with hardware and software teams to ensure seamless integration and optimization of AI models across edge devices and cloud infrastructure.

Key Responsibilities: Design and Architect Hardware Systems for AI Workloads: Lead the design of custom hardware solutions (e.g., GPUs, TPUs, FPGAs, ASICs) to accelerate machine learning models and improve overall system performance. Work with Hardware Engineers: Collaborate closely with hardware engineers to specify and design embedded systems and hardware platforms optimized for AI tasks. This includes defining hardware specifications, selecting components, and ensuring tight integration between hardware and software.

Hardware-Specific Model Optimization: Tune and optimize machine learning models to run efficiently on specialized hardware platforms. Focus on improving performance, memory utilization, and energy efficiency on hardware like GPUs, FPGAs, and ASICs. Edge Computing Hardware Development: Design and implement AI systems for edge devices, including IoT sensors, autonomous systems, robotics, and mobile platforms.

Ensure AI models run effectively in resource-constrained environments. Hardware-in-the-Loop (HIL) Testing: Lead HIL testing to simulate and validate the interaction between AI software and hardware, particularly for real-time systems like autonomous vehicles or robotic systems. Real-Time System Design and Optimization: Design real-time hardware systems that run AI models with minimal latency.

Focus on systems that require real-time decision-making such as autonomous vehicles, robotic control systems, and real-time data processing applications. Collaborate with Cross-Functional Teams: Work closely with data scientists, software engineers, and hardware teams to ensure AI models are optimized for the hardware they run on and integrated into production systems. Deploy Machine Learning Models on Custom Hardware: Lead the deployment of machine learning models on edge devices, embedded systems, and custom hardware.

This includes integrating models with hardware accelerators and ensuring real-time, reliable performance. Use Hardware-Specific ML Frameworks: Utilize frameworks like Xilinx Vitis AI, TensorRT, and TensorFlow Lite for deploying models to edge devices, FPGAs, and other hardware accelerators. AI Hardware Optimization: Optimize hardware for AI systems by improving energy efficiency, thermal management, and overall system performance.

Ensure AI models are running optimally on the available hardware. Monitor and Maintain Hardware Performance: Implement systems to continuously monitor AI model performance on hardware and develop strategies for hardware upgrades and maintenance. Required Skills and Qualifications: Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Mechatronics Engineering, or a related field. 5+ years of experience in systems engineering, with a focus on AI/ML and hardware integration, particularly in edge computing or embedded systems.

Experience with hardware platforms like GPUs (NVIDIA), TPUs, FPGAs, or ASICs for machine learning workloads. Strong proficiency in programming languages such as Python, C++, C, and familiarity with hardware description languages (e.g., HDL for custom hardware designs). Experience with machine learning frameworks like TensorFlow, PyTorch, or MXNet.

Experience with edge devices such as NVIDIA Jetson, Google Coral, Raspberry Pi, or similar embedded systems. Solid understanding of hardware architectures and how to optimize machine learning models for hardware accelerators. Experience in real-time systems design (e.g., autonomous vehicles, robotics, industrial IoT).

Knowledge of hardware simulation and prototyping tools (e.g., Xilinx Vitis AI, TensorRT, TensorFlow Lite, etc.). Excellent problem-solving skills and the ability to work in cross-disciplinary teams. Preferred Skills: Experience with hardware-specific machine learning frameworks such as Xilinx Vitis AI, NVIDIA TensorRT, TensorFlow Lite, and ONNX.

Experience with custom hardware design, including FPGA programming, ASIC design, or creating custom AI chips for specific applications. Experience working on autonomous systems or robotic systems where hardware integration with AI models is key. Experience in the automotive industry or other real-time systems like aviation, where hardware performance is critical for safety and reliability.

NOT AVAILABLE FOR C-C