Visionary AI Engineer for Performance Optimization

3 days ago

Bengaluru, Karnataka, India Pixis Full time

Pixis is a pioneering US-based codeless technology company that empowers brands to scale their performance marketing efforts through accessible AI. With a mission to develop powerful AI infrastructure, Pixis equips marketers worldwide with robust plug-and-play AI products and 200+ proprietary self-evolving AI models without requiring a single line of code.

Backed by recognized investors including SoftBank Vision Fund, Touring Capital, Grupo Carso, General Atlantic, Celesta Capital, and Chiratae Ventures, Pixis has raised $209M in funding across Series A, B, C, and C1. The company's customer base includes global brands such as DHL Express, Joe & The Juice, Claroshop, Allbirds, L'Oreal, HDFC Bank, Skoda, Swiggy, Clar, and SmartAsset.

Pixis' diverse team of 300+ professionals spread across 14 geographies focuses on building transformative AI products to maximize marketing and demand generation efforts. For an AI-driven experience, visit https://pixis.ai/

About the Role

We are seeking a highly skilled Machine Learning Engineer with 3+ years of experience in optimizing deep learning model performance, particularly in computer vision and natural language processing. The ideal candidate will have a strong background in software engineering, deep learning, and performance optimization techniques.

  • 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 and Qualifications

The successful candidate will possess:

  • 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.
Estimated Salary Range

$150,000 - $200,000 per year

The estimated salary range is based on industry standards, location, and job requirements. The actual salary may vary depending on factors such as the candidate's level of experience, qualifications, and performance.

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