Deep Learning Performance Engineer

4 weeks ago

bangalore, India Pixis Full time
About Pixis:
Pixis is 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. Since its inception, Pixis has been on a mission to develop powerful AI infrastructure that equips marketers across countries with robust plug-and-play AI products, 200+ proprietary, self-evolving AI models without having to write a single line of code. The company has raised a total funding of $209M across Series A, B, C and C1, and is backed by recognized investors including SoftBank Vision Fund, Touring Capital, Grupo Carso, General Atlantic, Celesta Capital and Chiratae Ventures.
Our customer base includes global brands such as DHL Express, Joe & The Juice, Claroshop, Allbirds, L’Oreal, HDFC Bank, Skoda, Swiggy, Clar and SmartAsset, to name a few Today Pixis’ talented and diverse team of 300+ spread across over 14 geographies is focused on building incredibly transformative AI products to help customers get the most out of their marketing and demand generation efforts.
Get ready to embark on an AI venture at Description
We are looking to hire a Deep Learning Performance Engineer with 3+ years of relevant experience in optimizing the performance of deep
learning models, particularly in the fields of computer vision and natural language processing. The ideal candidate will have a strong background in software engineering, deep learning, and performance optimization techniques.
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.
Skills and Technologies Required:
Deep Learning and Machine Learning : Extensive experience with PyTorch and familiarity with TensorFlow. Understanding of deep learning architectures and ability to implement efficient models.
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.
Integration : Understanding of Python API Frameworks such as FastAPI. Capability to build robust and fast synchronous or asynchronous APIs. Experience working with queuing systems and caching systems.
Fine-Tuning : Knowledge of strategies for fine-tuning deep learning models to improve performance on specific tasks or datasets.
Bonus Skills:
Experience with CUDA and GPU programming for deep learning.
Familiarity with deep learning deployment on cloud platforms (AWS, GCP, Azure).
Understanding of container technologies like Docker and orchestration tools like Kubernetes.
DevOps and Cloud Skills: Experience with version control systems, preferably Git.
Familiarity with CI/CD pipelines and automation tools (e.g., Jenkins, GitHub Actions).
Knowledge of cloud computing services and deployment (AWS services like EC2, S3, Lambda, etc.).
The ideal candidate will be 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.

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