ML Compiler Engineer - Embedded AI & Hardware

Qualcomm; Remote; GBP 50,000 - 80,000; Full time

Job summary

A leading technology innovator is seeking a Machine Learning Engineer to create and implement machine learning techniques. You'll develop advanced hardware and software solutions while collaborating with cross-functional teams. The ideal candidate will possess a strong background in machine learning frameworks and programming languages, along with relevant engineering experience. This role offers a significant opportunity to influence machine learning applications across a variety of technological domains in a dynamic environment.

Qualifications

• Experience in Machine Learning frameworks like Tensor Flow or Pytorch.
• Minimum 2 years in relevant engineering roles.
• Experience with low-level OS interactions is a plus.

Responsibilities

• Extend training frameworks with new features and optimizations.
• Develop advanced machine learning hardware and software.
• Enable AI models on hardware through optimized software.
• Prototype novel machine learning solutions.

Skills

Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras)

Embedded system development

Programming languages (e.g., Python, R, C, C++)

Statistics and probability

Collaboration in matrixed organizations

Low-level interactions with operating systems

Education

Bachelor's degree in Computer Science or related field

Master's degree in Computer Science or related field

PhD in Computer Science or related field

Job description

A leading technology innovator is seeking a Machine Learning Engineer to create and implement machine learning techniques. You'll develop advanced hardware and software solutions while collaborating with cross-functional teams. The ideal candidate will possess a strong background in machine learning frameworks and programming languages, along with relevant engineering experience. This role offers a significant opportunity to influence machine learning applications across a variety of technological domains in a dynamic environment.