--- title: "Usamah Zaheer - Machine Learning Software Engineer at Arm" description: "Machine Learning Software Engineer at Arm in Cambridge, UK. Writes about edge ML inference, ML compilers, robotics and AI systems." canonical_url: "https://www.usamah.me" author: "Usamah Zaheer" type: "profile" date_modified: "2026-09-15" --- # Usamah Zaheer Machine Learning Software Engineer at Arm. Cambridge, United Kingdom. > Machine Learning Software Engineer at Arm in Cambridge, UK. Writes about edge ML inference, ML compilers, robotics and AI systems. Machine Learning Engineer with 4+ years of experience in deep learning, computer vision, and edge computing. Strong background in end-to-end ML development, model optimisation, and MLOps practices. Usamah Zaheer is a Machine Learning Software Engineer at Arm, where he designs and optimises ML infrastructure for Arm architectures. He is pursuing an MS in Artificial Intelligence at the University of Texas at Austin and holds an MS in Embedded Systems from the University of Leicester. His work spans deep learning, computer vision, edge computing, robotics, and MLOps across companies including Arm, Dyson, and the University of Leicester. ## Book ### How to Make Your Model Fast *A Systems View of Efficient Machine Learning, from Silicon to Agents* A book in fourteen parts on making models run inside real budgets for latency, memory, power and cost. Rooflines and Arm vector units, kernels and compilers, quantisation and compression, then vision, on-device language models, robotics, profiling, serving and agents. Free to read at https://ai.usamah.me. ## Experience ### Machine Learning Software Engineer, Arm Mar 2025 – Present - Designed and optimised ML infrastructure to analyse and enhance performance of models and systems on Arm architectures. - Optimised ML compilers and libraries for Arm, improving inference performance and reducing latency. - Conducted deep kernel-level analysis to identify and eliminate inference bottlenecks. - Profiled and optimised runtime performance of ML models; developed scalable benchmarking solutions across cloud and edge environments. - Built automated pipelines for data collection, preprocessing, and model evaluation, streamlining production workflows. - Led cross-functional collaborations to align ML infrastructure with organisational goals, owning a new project from inception. Stack: PyTorch, TensorFlow, JAX, FBGEMM, KleidiAI, ACL, ArmNN, OneDNN. ### Robotics Software Engineer, Dyson Sep 2022 – Aug 2024 - Developed CNN algorithms for segmentation, object detection, and classification, applying quantisation, pruning, and knowledge distillation for deployment on robot hardware. - Architected evaluation tools and robotics algorithms for planning and navigation using C++. Conducted log analysis, debugging, and on-robot testing. - Deployed models on diverse hardware and edge devices, optimising through profiling and bottleneck analysis using CUDA and cuDNN. - Developed a VLM solution that saved over £100,000 and boosted productivity by 20x. - Streamlined the ML lifecycle with model versioning, monitoring, and automated deployment using CI/CD pipelines. - Presented complex ML projects to senior leaders and the CEO. Stack: PyTorch, MXNet, ONNX, CUDA, cuDNN, C++. ### ML Research Assistant, University of Leicester Mar 2021 – Aug 2022 - Spearheaded development of an end-to-end automated ML pipeline for processing high-resolution data in real-time. - Integrated cutting-edge CNNs in PyTorch and TensorFlow for high-resolution satellite imagery analysis. - Led development of ML systems utilising Random Forest and SVM for predictive modelling. - Deployed AI solutions in cloud environments with Docker and Kubernetes. Stack: PyTorch, TensorFlow, Scikit-learn, Docker, Kubernetes, R. ## Projects ### AI Agent Systems, Stealth Startup Aug 2024 – Feb 2025 - Led design and implementation of AI agents for SDRs, integrating NLP and LLMs for accurate and scalable solutions. - Managed cloud infrastructure on Vertex AI with Databricks and Snowflake, utilising RAG to enhance AI response precision. - Orchestrated ML workflows and semantic search using LangChain and LlamaIndex. - Oversaw containerised deployment with Docker and Kubernetes, implementing MLOps practices with MLflow. ### ML Model Deployment App - Developed an Android application for deploying ML models on edge devices for object detection using YOLO, Mask R-CNN, and SSD. - Optimised using TensorFlow Lite with quantisation and pruning for low-latency, on-device inference. ### 360 Vision Navigation, Dyson - Developed a robot utilising 26 sensors with SLAM technology and 360-degree vision for autonomous navigation. - Advanced path planning algorithms and developed integration and unit tests in C++ and Python. ### Air Purifiers Embedded Software, Dyson - Contributed to embedded software for all Dyson air purifiers including Pure Cool using C and Python. - Designed backbone logic behaviour system for hardware communication with FreeRTOS. Updated a fundamental library stack for 10+ projects. ### AI4EO & CNN Research, University of Leicester - Classified high-resolution satellite images for forest fire detection using CNNs, Random Forest, and SVM. - Evaluated CNN architectures for autonomous vehicle applications, optimised with Transfer Learning and TensorRT. Written up in full at https://www.usamah.me/projects (Markdown: https://www.usamah.me/projects.md). ## Education ### MS in Artificial Intelligence University of Texas at Austin, USA. Jan 2025 – Dec 2026. ### MS in Embedded Systems and Control Engineering University of Leicester, England, Distinction. Jan 2021 – Aug 2022. Thesis: Performance Evaluation of Deep Learning Techniques for Object Detection in Autonomous Vehicles. ### BTech in Electronics and Communication Engineering Jawaharlal Nehru Technological University, India, First Class. Aug 2016 – Oct 2020. ## Skills - **Languages:** Python, C++, C, Rust, SQL, MATLAB - **ML & Deep Learning:** PyTorch, TensorFlow, JAX, Scikit-learn, LLMs, VLMs, Multimodal Models, CNNs, Transformers - **Computing & Inference:** TensorRT, CUDA, cuDNN, ArmNN, LLVM, GGML, OpenMP, ONNX/Runtime, FBGEMM, KleidiAI - **Profiling & Debugging:** NVIDIA Nsight, PyTorch Profiler, TensorFlow Profiler, Valgrind, gprof, cProfile, Py-Spy - **Cloud & Deployment:** AWS, GCP, Vertex AI, Kubernetes, Docker, GitHub Actions, Jenkins, MLflow, KubeFlow, Databricks, Snowflake - **Data & Visualisation:** Pandas, Apache Spark, Matplotlib, Plotly, Streamlit, Grafana, Tableau, Gradio ## Leadership - Led an entire project from inception to completion independently at Arm. - Presented projects to the Dyson CEO and senior leadership. - Mentored undergraduates in robotics and machine learning. - Participated in 10+ hackathons and workshops. - Open-source contributor with a portfolio of projects on GitHub. ## Contact - Email: usamahzaheer155@gmail.com - LinkedIn: https://linkedin.com/in/usamahzaheer - GitHub: https://github.com/usamahz ## Posts - [We Can All Bake Bread](https://www.usamah.me/blog/we-can-all-bake-bread) (2026-09-14): Usamah Zaheer on why AI letting anyone build their own software will not kill software companies. Everyone can bake bread and almost everyone still buys it, because nobody wants to be the baker. - [AI Is Making Everyone Sound the Same](https://www.usamah.me/blog/ai-is-making-everyone-sound-the-same) (2026-08-24): Usamah Zaheer argues that when everyone runs their thinking through the same models, outputs converge, and the scarce skill shifts from generating an answer to knowing whether it is any good. - [Stop Latency Laundering](https://www.usamah.me/blog/latency-laundering) (2026-08-10): Usamah Zaheer names latency laundering, the habit of moving delay outside the measurement boundary until a slow system reports a fast number. - [The Next Trillion-Dollar AI Opportunity Is Not Bigger Models](https://www.usamah.me/blog/trillion-dollar-ai-opportunity) (2026-08-10): Usamah Zaheer argues the next trillion-dollar AI opportunity is not another model lab. It is the infrastructure that makes intelligence cheap enough to run on billions of devices. - [Inside the Black Box: ML Compiler Optimisation from a Practitioner at Arm](https://www.usamah.me/blog/compiler-optimization-for-ml) (2025-04-15): Usamah Zaheer explains ML compiler optimisation from a practitioner perspective at Arm, covering graph optimisation, kernel selection, and the profiling feedback loop. - [Why Edge ML Inference is the Next Frontier](https://www.usamah.me/blog/edge-ml-inference) (2025-03-20): Usamah Zaheer explains why on-device ML inference is the future, drawing on experience optimising models at Arm and deploying perception systems at Dyson. - [I Gave a Robot Eyes and a Brain: VLMs in Real-World Robotics](https://www.usamah.me/blog/vlms-in-robotics) (2025-02-15): The gap between a VLM demo on Twitter and a VLM running on a real robot is enormous. Usamah Zaheer shares what he learned closing that gap at Dyson. - [Applying CNNs to Satellite Imagery: Lessons from Forest Fire Detection](https://www.usamah.me/blog/satellite-imagery-cnns) (2025-02-01): Usamah Zaheer shares lessons from applying CNNs to high-resolution satellite imagery for forest fire detection at the University of Leicester. - [What I Learned Building AI Agents at a Stealth Startup](https://www.usamah.me/blog/building-ai-agents) (2025-01-10): Usamah Zaheer built AI agent systems from scratch at a stealth startup. Here is what the hype gets right, what it gets wrong, and why he chose to go back to school. Full text of every post: https://www.usamah.me/llms-full.txt ## This page in other formats - Canonical HTML: https://www.usamah.me - Markdown: the same path with `.md` appended - Site index for models: https://www.usamah.me/llms.txt - Whole site as one file: https://www.usamah.me/llms-full.txt - Structured CV: https://www.usamah.me/profile.json - Endpoint manifest: https://www.usamah.me/agents.json