We are looking for a skilled AI/ML Algorithm Engineer to develop and optimize machine learning algorithms for deployment on hardware platforms. The primary responsibilities include optimizing machine learning models, performing model quantization, and ensuring efficient deployment on resource-constrained hardware. The ideal candidate will have expertise in deep learning, transformer models, AI applications, and will collaborate closely with the hardware team to deliver high-performance AI solutions tailored to specific platforms.

 

Essential Skills: 

Proficiency in deep learning frameworks such as TensorFlow, PyTorch, and ONNX.  Strong understanding of transformer models and their optimization techniques.  Experience in model quantization, pruning, and hardware-aware neural architecture search (NAS).  Knowledge of AI for computer vision, including convolutional neural networks (CNNs) and vision transformers (ViTs).  Strong programming skills in Python, with experience in C/C++ for performance-critical applications.  Understanding of hardware architectures, including GPUs, TPUs, and AI accelerators.  Excellent problem-solving skills and the ability to debug complex AI models on hardware. 

Required Tools: 

TensorFlow or PyTorch  Jupyter Notebook  MATLAB  Scikit-learn  ONNX 

Desirable Skills: 

Experience with edge AI and deployment on embedded platforms (e.g., Jetson Nano, Raspberry Pi, Coral).  Familiarity with energy-efficient AI techniques and low-power hardware optimization.  Understanding of compiler optimization techniques for AI workloads. 

Education and Experience: 

Bachelor’s Degree in Computer Science, Electrical Engineering, Data Science, or a related field (Master’s or PhD preferred).  3+ years of experience in developing and deploying machine learning models.  Proven track record of optimizing AI/ML algorithms for hardware platforms.  Experience with computer vision applications and transformer-based models is highly desirable. 
 

Responsibilities: 

Design, optimize, and implement machine learning algorithms for deployment on hardware platforms.  Perform model quantization, pruning, and compression to enable efficient execution on resource-limited devices.  Optimize transformer models and other deep learning architectures for performance and energy efficiency.  Collaborate with hardware engineers to align AI applications with platform capabilities and constraints.  Implement and deploy models on hardware platforms such as TPUs, NPUs, and embedded systems.