CCG CGAI: AI Software Solutions Engineer

Job Description

Works with internal engineering teams and external partners, customers, and ISVs to deliver optimized AI solutions using Intel products with the goal of driving adoption of Intel products. Researches technical trends and utilizes deep domain and market segment expertise to prototype solutions and develop AI software including open source libraries and models. Optimizes performance of AI models through deep knowledge and expertise of AI frameworks, algorithms, models, and related hardware. Researches, develops, and adjusts new or existing AI models, code, parameters, and/or quantization to address issues and modify operations to enhance performance. Partners with AI algorithm and framework engineers as needed to optimize endtoend AI models to Intel hardware features. Serves as a trusted technical advisor and provides technical enabling. Partners with Intel software and hardware product development teams to accelerate and optimize future products in AI and HPC domains by leading application preenabling and product hardening. Delivers competitive and differentiated benchmark collateral and identify and drive key workloads into product requirements and architect innovative projection methodologies and tools.

Qualifications

Responsibilities: 1.Driving design for a compilation flow that will scale to future Neural Network architectures and target platforms. 2.Implementing compilation passes for enabling and optimizing Neural Networks workloads. 3.Development of performance analysis, visualization, and profiling tools. 4.Integrating and testing changes, owning the tasks execution end-to-end. Qualifications: 1.BS/MS in Computer Science or a similar field. 2.At least 2 years of experience in programming. 3.Excellent C++ programming skills. 4.Some production software engineering background, experience with CI, code reviews, paired programming, unit and integration testing. Preferred Qualifications: 1.Experience in one the following: compiler technologies, computer vision, numerical modelling, high-performance computing, deep-learning frameworks or algorithms. 2.Experience in AI hardware accelerators, GPU, heterogeneous architectures software development. 3.Experience in mapping between Neural Networks architectures and hardware accelerated inference. 4.Experience in LLVM/MLIR. 5.Python programming skills.