Deep Learning Optimization Specialist
2 days ago
About Pixis:
Pixis is a pioneering US-based technology company that specializes in developing cutting-edge, accessible AI solutions to empower brands to elevate their performance marketing efforts and enhance decision-making processes seamlessly.
Since its inception, Pixis has been dedicated to creating powerful AI infrastructure that equips marketers worldwide with robust, plug-and-play AI products, 200+ proprietary, self-evolving AI models without requiring a single line of code.
The company has secured a total funding of $209M across Series A, B, C, and C1, and is backed by prominent investors including SoftBank Vision Fund, Touring Capital, Grupo Carso, General Atlantic, Celesta Capital, and Chiratae Ventures.
Pixis boasts an impressive customer base consisting of global brands such as DHL Express, Joe & The Juice, Claroshop, Allbirds, L'Oreal, HDFC Bank, Skoda, Swiggy, Clar, and SmartAsset, among others.
Today, Pixis' talented and diverse team of 300+ spread across over 14 geographies is focused on developing transformative AI products to help customers maximize the potential of their marketing and demand generation efforts.
Our mission at Pixis is to revolutionize the AI landscape and push the boundaries of what is possible. We are driven by a passion for innovation and a commitment to delivering exceptional results.
We are currently seeking an experienced Deep Learning Optimization Specialist with 3+ years of relevant expertise in optimizing the performance of deep learning models, particularly in the fields of computer vision and natural language processing.
Responsibilities:
- Optimize deep learning model inference runtime and quality for high-performance computing environments.
- 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.
- Implement fine-tuning strategies to improve model performance on specific datasets or tasks.
- Utilize inference frameworks like TensorRT, ONNX, and ONNXRuntime to accelerate and streamline model deployments.
Skills and Technologies Required:
- Deep Learning and Machine Learning: Extensive experience with PyTorch and familiarity with TensorFlow.
- Inference Frameworks: Proficiency in using TensorRT, ONNX, and ONNXRuntime for model optimization and deployment.
- Model Optimization: Strong knowledge of techniques for optimizing model inference runtime and quality, including quantization, pruning, and knowledge distillation.
- Computer Vision Models: In-depth understanding of modern computer vision deep learning models like Stable Diffusion, ControlNet, and LAMA.
- Software Engineering: Excellent programming skills in Python and experience in writing high-quality, maintainable code.
- Performance Engineering: Expertise in profiling, benchmarking, and optimizing software and algorithms for maximum performance.
- Fine-Tuning: Knowledge of strategies for fine-tuning deep learning models to improve performance on specific tasks or datasets.
We offer a highly competitive salary of approximately $180,000 per year, along with a comprehensive benefits package that includes medical, dental, and vision coverage, as well as generous paid time off and holidays.
If you are an ambitious professional with a proven track record of improving model performance and are passionate about pushing the boundaries of AI technology, we would love to hear from you.
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