Senior AI Software Engineer

microTECH Global Limited; Cambridge; On-site; GBP 60,000 - 90,000; Full time

Job summary

A leading technology company in Cambridge is seeking a Senior AI Software Engineer to design and implement optimizations for real-time AI applications. Candidates should have substantial experience in optimizing AI chip architectures and knowledge of heterogeneous computing. This role requires collaboration with cross-functional teams to shape AI solutions and innovative technological strategies.

Qualifications

• Rich experience in AI chip architectures, familiar with heterogeneous computing architectures.
• Hands-on experience in Numerical Calculation, Compilation, Algorithm & chip co-design.
• Knowledge of AI industry application scenarios and algorithm development trends.

Responsibilities

• Design and implement optimizations to enable real-time AI applications.
• Collaborate with cross-functional teams on AI solutions.
• Identify technologies related to NPU chips and develop evolution strategies.

Skills

Optimizing AI chip architectures

GPU compute APIs (CUDA, OpenCL)

Software development using C/C++ and Python

Job description

Overview

Job Title: Senior AI Software Engineer

Location: Cambridge - Permanent

Responsibilities

• As a Senior Software Engineer in the AI Processor Software & Hardware Co-design Lab, you will be responsible for designing and implementing both compile-time and run-time optimizations to enable real-time AI applications on AI processors. You will collaborate closely with cross-functional teams to integrate and deploy AI solutions on the Ascend platform, leveraging your expertise to shape the performance, functionality, and efficiency of our AI models and systems.
• Be responsible for one of the sub technical direction of AI Processor Software & Hardware Co-design Lab, identify key root technologies related to NPU chips, develop evolution strategies and roadmaps, promote and implement the evolution strategies to build industry-leading technical competitiveness, support business success in the computing field.
• Carry out technology and business innovation, integrate several sub-domains of application algorithms, frameworks, runtime, modelling and simulation, and compilers from the perspective of processors, and build end-to-end architecture competitiveness.
• Grasp the AI industry and technology trends, gain insight into the development direction of AI applications and algorithms, develop key technical architectures of basic AI software and hardware, and resolve key usability and performance issues in full-stack AI through technical projects.

Required

• Rich experience in optimizing AI chip architectures and AI systems, be familiar with mainstream heterogeneous computing software and hardware architectures in the industry, and have comprehensive capabilities from applications to basic software to chips.
• Hands-on experience of one of the following technologies: Numerical Calculation, Compilation, Algorithm & chip co-design, Runtime, Shared Memory.
• Knowledge of AI industry application scenarios, be familiar with mainstream models and algorithm development trends, and be able to extract requirements for the chip layer.
• Experience in analyzing workload sensitivity to micro-architecture features, evaluating performance trade-offs, and recommending improvements to both micro-architecture and application software for optimal efficiency.
• Familiarity with the performance impact of different compute, memory, and communication configurations, as well as hardware and software implementation choices, on AI acceleration.
• Experience with GPU compute APIs such as CUDA or OpenCL, and the ability to utilize GPU/NPU-optimized libraries to enhance performance.
• Experience in the development of deep learning frameworks, compilers, or system software.
• Strong background in compilers and optimization techniques; experience with LLVM-MLIR is a plus, but not required.
• Experience in software development using C/C++ and python.

Desired

• Relevant experience in several sub-fields of AI application algorithms, frameworks, runtime, modelling and simulation, and compilers.
• In-depth understanding of the innovative methods, platforms, and tools of AI head manufacturers, and have experience in transforming application and academic research achievements into commercial products.
• Experience with GPU acceleration using AMD or Nvidia GPUs.
• Experience in developing inference backends and compilers for GPU or NPU.
• Experience with AI/ML inference frameworks like ONNXRuntime, IREE or TVM.
• Experience with deploying AI models in production environments.