About Usamah Zaheer
- Role
- Machine Learning Software Engineer at Arm
- Based in
- Cambridge, United Kingdom
- Focus
- Edge ML inference, ML compilers, model optimisation, computer vision and robotics
- Experience
- 4+ years across Arm, Dyson, University of Leicester
- Studying
- MS in Artificial Intelligence, University of Texas at Austin (expected Dec 2026)
- Wrote
- How to Make Your Model Fast, free at https://ai.usamah.me
- Contact
- usamahzaheer155@gmail.com
I'm Usamah Zaheer, a Machine Learning Software Engineer at Arm in Cambridge, UK, where I design and optimise ML infrastructure for Arm architectures. My work focuses on making deep learning models run faster and more efficiently - from optimising ML compilers and libraries to conducting deep kernel-level analysis that eliminates inference bottlenecks.
Before Arm, I spent two years at Dyson as a Robotics Software Engineer. There, I developed CNN algorithms for segmentation, object detection, and classification - deploying them on robot hardware using techniques like quantisation, pruning, and knowledge distillation. I also built a VLM solution that saved over £100,000 and boosted productivity by 20x. One of the highlights was presenting my work directly to the CEO and senior leadership.
My journey in ML research started at the University of Leicester, where I worked as an ML Research Assistant. I built end-to-end automated ML pipelines for processing high-resolution satellite imagery in real-time, integrating CNNs in PyTorch and TensorFlow. I also led development of predictive models using Random Forest and SVM, and deployed AI solutions in cloud environments with Docker and Kubernetes.
I'm currently pursuing an MS in Artificial Intelligence at the University of Texas at Austin, building on my MS in Embedded Systems and Control Engineering from the University of Leicester (where I graduated with Distinction) and my BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University.
My technical toolkit spans the full ML stack: PyTorch, TensorFlow, JAX, CUDA, TensorRT, ArmNN, and C++ for performance-critical work. I'm passionate about the intersection of ML and hardware - making models not just accurate, but fast and deployable at the edge.
Between Dyson and Arm, I led the design and implementation of AI agents at a stealth startup, integrating LLMs and NLP for scalable solutions using RAG, LangChain, and LlamaIndex. I managed cloud infrastructure on Vertex AI and oversaw containerised deployments with Docker and Kubernetes.
Outside of work, I mentor undergraduates in robotics and machine learning, participate in hackathons, and contribute to open-source projects. You can find my work on GitHub, or connect with me on LinkedIn.
I wrote a free book about all of this, How to Make Your Model Fast: fourteen parts on making models run inside real budgets for latency, memory, power and cost, from rooflines and Arm vector units through kernels, compilers, quantisation and compression to vision, on-device language models, robotics, profiling, serving and agents.
Explore my projects to see what I've built, or check out my blog where I write about machine learning, edge computing, and software engineering.
The views in this posting reflect my personal views and do not represent the views of my employer or hiring entity.
Expertise
- Edge ML inference
- Quantisation (PTQ, QAT, mixed-precision), operator fusion, memory planning and kernel optimisation for Arm Cortex-A and Cortex-M, Mali GPU and Ethos NPU.
- ML compilers and libraries
- ArmNN, Arm Compute Library (ACL), KleidiAI, FBGEMM, OneDNN, TensorRT and ONNX Runtime.
- Deep learning frameworks
- PyTorch, TensorFlow, JAX and ONNX.
- Computer vision
- CNNs, VLMs, object detection, segmentation, classification and knowledge distillation.
- AI agents
- LLM-based agent systems, retrieval-augmented generation, semantic search and cost-aware routing.
- MLOps and infrastructure
- Docker, Kubernetes, Vertex AI, MLflow, Databricks and CI/CD pipelines.
- Profiling
- NVIDIA Nsight, PyTorch Profiler, Valgrind, gprof and Arm Streamline.
Frequently Asked Questions
Who is Usamah Zaheer?
Usamah Zaheer is a Machine Learning Software Engineer at Arm, based in Cambridge, UK. He has over 4 years of experience in deep learning, computer vision, edge computing, and MLOps. He previously worked at Dyson as a Robotics Software Engineer.
What does Usamah Zaheer work on?
Usamah works on designing and optimising ML infrastructure for Arm architectures. He specialises in ML compilers, inference performance, deep learning frameworks (PyTorch, TensorFlow, JAX), and deploying models on edge devices.
Where did Usamah Zaheer study?
Usamah is currently pursuing an MS in Artificial Intelligence at the University of Texas at Austin. He holds an MS in Embedded Systems and Control Engineering from the University of Leicester (Distinction) and a BTech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University.
Where has Usamah Zaheer worked?
Usamah has worked at Arm (Machine Learning Software Engineer), Dyson (Robotics Software Engineer), the University of Leicester (ML Research Assistant), and a stealth startup (AI Agent Systems). He has also contributed to open-source projects.
What book has Usamah Zaheer written?
Usamah Zaheer wrote "How to Make Your Model Fast: A Systems View of Efficient Machine Learning, from Silicon to Agents", a free book in fourteen parts covering roofline analysis, Arm hardware, kernels, ML compilers, quantisation, compression, vision, on-device language models, robotics, profiling, serving and agents. It is free to read at https://ai.usamah.me.
What does Usamah Zaheer write about?
Usamah writes about edge ML inference, ML compilers, model optimisation, robotics and vision-language models, and the economics of AI systems. His posts are at https://www.usamah.me/blog and his book on efficient machine learning is at https://ai.usamah.me.
How can I contact Usamah Zaheer?
Email usamahzaheer155@gmail.com, or reach him on LinkedIn at https://linkedin.com/in/usamahzaheer. His code is on GitHub at https://github.com/usamahz.