AI Optimization Specialist
18 hours ago
We are seeking an experienced AI Optimization Specialist to join our team at Pixis, a US-based codeless technology company that develops accessible AI to empower brands to scale their performance marketing efforts and augment their decision-making seamlessly. With a total funding of $209M across Series A, B, C, and C1, and backed by recognized investors including SoftBank Vision Fund, Touring Capital, Grupo Carso, General Atlantic, Celesta Capital, and Chiratae Ventures, Pixis has established itself as a leader in the AI industry.
As an AI Optimization Specialist, you will play a critical role in optimizing the performance of deep learning models, particularly in the fields of computer vision and natural language processing. You will work with cutting-edge deep learning models such as Stable Diffusion, ControlNet, and LAMA, focusing on computer vision tasks, and utilize inference frameworks like TensorRT, ONNX, and ONNXRuntime to accelerate and streamline model deployments.
The ideal candidate will have a strong background in software engineering, deep learning, and performance optimization techniques, with extensive experience with PyTorch and familiarity with TensorFlow. They will be proficient in using TensorRT, ONNX, and ONNXRuntime for model optimization and deployment, and have strong knowledge of techniques for optimizing model inference runtime and quality, including quantization, pruning, and knowledge distillation.
This is an exciting opportunity for a problem-solver who thrives in a fast-paced environment and is passionate about pushing the boundaries of AI technology. If you are excited about the opportunity to work on state-of-the-art deep learning projects and have a proven track record of improving model performance, we would love to hear from you.
About the Role:
**Job Responsibilities:**
Optimize deep learning model inference runtime and quality for high-performance computing environments.
Work with cutting-edge deep learning models such as Stable Diffusion, ControlNet, and LAMA, focusing on computer vision tasks.
Utilize inference frameworks like TensorRT, ONNX, and ONNXRuntime to accelerate and streamline model deployments.
Implement fine-tuning strategies to improve model performance on specific datasets or tasks.
Collaborate with the AI research and development team to integrate the latest advancements in deep learning into our projects.
Conduct rigorous performance testing and benchmarking to ensure that models meet the required efficiency and accuracy standards.
Provide technical guidance on best practices for deep learning model optimization and deployment.
**Requirements:**
* 3+ years of relevant experience in optimizing the performance of deep learning models, particularly in the fields of computer vision and natural language processing.
* Strong background in software engineering, deep learning, and performance optimization techniques.
* Extensive experience with PyTorch and familiarity with TensorFlow.
* Proficiency in using TensorRT, ONNX, and ONNXRuntime for model optimization and deployment.
* Strong knowledge of techniques for optimizing model inference runtime and quality, including quantization, pruning, and knowledge distillation.
What We Offer:
We offer a competitive salary range of $180,000 - $220,000 per year, depending on experience.
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