Graduate ML Engineer: Hardware-Aware AI on CPUs/GPUs/NPUs

Arm Limited; Lunds kommun; Hybrid; SEK 500,000 - 611,000; Full time

Benefits offered by this job

Relocation support

Visa sponsorship

Graduate Committee involvement

Global Graduate Conference (GGC)

Grad-teach-Grads workshops

Salary reviews twice a year

Job summary

Arm Limited, through the Central Technology Machine Learning team, invites graduates to explore ML/AI workloads on Arm compute platforms. You will study performance, power and area, and develop hardware-aware optimizations for AI models across CPU, GPU and NPU architectures.

The role emphasizes Python/software development, model training/deployment with PyTorch or TensorFlow, and building tools to accelerate research and architecture exploration in a dynamic hybrid environment.

Qualifications

• Master’s or PhD in Computer Engineering, Electrical Engineering, Computer Science or related field.
• Experience programming in Python and software development; hardware/software design knowledge.
• Experience training and deploying ML/AI models using PyTorch or TensorFlow.
• Strong technical communication skills.

Responsibilities

• Develop in-depth understanding of ML/AI workloads on Arm compute platforms with focus on PPA (performance, power, area).
• Analyze use cases to drive ML/AI workload support in Arm IP development.
• Develop hardware-aware optimization algorithms to run AI models on Arm CPUs/GPUs/NPUs.
• Develop internal tooling to support algorithmic and architecture exploration.

Skills

Python

ML/DL frameworks

Hardware design

Technical communication

Model deployment

Education

Master’s or PhD in Computer Engineering, Electrical Engineering, Computer Science

Tools

PyTorch

TensorFlow

Job description

Arm Limited, through the Central Technology Machine Learning team, invites graduates to explore ML/AI workloads on Arm compute platforms. You will study performance, power and area, and develop hardware-aware optimizations for AI models across CPU, GPU and NPU architectures.

The role emphasizes Python/software development, model training/deployment with PyTorch or TensorFlow, and building tools to accelerate research and architecture exploration in a dynamic hybrid environment.