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  "name": "Usamah Zaheer",
  "alternateNames": [
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    "Usamah Z"
  ],
  "headline": "Machine Learning Software Engineer at Arm",
  "location": {
    "city": "Cambridge",
    "country": "United Kingdom"
  },
  "email": "usamahzaheer155@gmail.com",
  "url": "https://www.usamah.me",
  "summary": "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.",
  "bio": "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.",
  "updated": "2026-09-15",
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  "experience": [
    {
      "title": "Machine Learning Software Engineer",
      "organisation": "Arm",
      "organisationUrl": "https://www.arm.com",
      "start": "2025-03",
      "end": null,
      "current": true,
      "period": "Mar 2025 – Present",
      "highlights": [
        "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"
      ]
    },
    {
      "title": "Robotics Software Engineer",
      "organisation": "Dyson",
      "organisationUrl": "https://www.dyson.com",
      "start": "2022-09",
      "end": "2024-08",
      "current": false,
      "period": "Sep 2022 – Aug 2024",
      "highlights": [
        "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++"
      ]
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    {
      "title": "ML Research Assistant",
      "organisation": "University of Leicester",
      "organisationUrl": "https://le.ac.uk",
      "start": "2021-03",
      "end": "2022-08",
      "current": false,
      "period": "Mar 2021 – Aug 2022",
      "highlights": [
        "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"
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    {
      "name": "AI Agent Systems",
      "organisation": "Stealth Startup",
      "start": "2024-08",
      "end": "2025-02",
      "summary": [
        "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."
      ],
      "detail": [
        "Led the design and implementation of AI agents for Sales Development Representatives (SDRs), integrating NLP and LLMs to build accurate and scalable solutions. The system leveraged RAG (Retrieval-Augmented Generation) to enhance AI response precision by grounding outputs in domain-specific knowledge bases.",
        "Managed cloud infrastructure on Google Vertex AI, orchestrating data pipelines with Databricks and Snowflake. Built semantic search and ML workflow orchestration using LangChain and LlamaIndex, enabling the agents to retrieve and reason over large document corpora.",
        "Oversaw containerised deployment with Docker and Kubernetes, implementing MLOps practices with MLflow for experiment tracking, model versioning, and reproducible deployments."
      ],
      "stack": [
        "LLMs",
        "RAG",
        "LangChain",
        "LlamaIndex",
        "Vertex AI",
        "Databricks",
        "Snowflake",
        "Docker",
        "Kubernetes",
        "MLflow"
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    },
    {
      "name": "ML Model Deployment App",
      "organisation": null,
      "start": null,
      "end": null,
      "summary": [
        "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."
      ],
      "detail": [
        "Developed an Android application for deploying machine learning models directly on edge devices, enabling real-time object detection using YOLO, Mask R-CNN, and SSD architectures. The app was designed for low-latency, on-device inference without requiring cloud connectivity.",
        "Optimised models using TensorFlow Lite with quantisation and pruning techniques, significantly reducing model size and inference time while maintaining detection accuracy. The project demonstrated practical deployment of state-of-the-art detection models on resource-constrained mobile hardware."
      ],
      "stack": [
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        "Mask R-CNN",
        "SSD",
        "TensorFlow Lite",
        "Android",
        "Edge Deployment"
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    {
      "name": "360 Vision Navigation",
      "organisation": "Dyson",
      "start": null,
      "end": null,
      "summary": [
        "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."
      ],
      "detail": [
        "Developed a robot utilising 26 sensors with SLAM (Simultaneous Localisation and Mapping) technology and 360-degree vision for fully autonomous navigation. The system fused data from multiple sensor modalities to build real-time environmental maps and navigate complex indoor environments.",
        "Advanced path planning algorithms to optimise navigation efficiency and obstacle avoidance. Developed comprehensive integration and unit tests in C++ and Python, ensuring reliability and robustness of the navigation stack across diverse environments and edge cases."
      ],
      "stack": [
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        "Computer Vision",
        "C++",
        "Python",
        "Sensor Fusion",
        "Path Planning"
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      "name": "Air Purifiers Embedded Software",
      "organisation": "Dyson",
      "start": null,
      "end": null,
      "summary": [
        "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."
      ],
      "detail": [
        "Contributed to the embedded software powering all Dyson air purifiers, including the Pure Cool product line. Worked with C and Python to develop firmware that manages sensor data processing, device communication, and real-time control logic.",
        "Designed the backbone logic behaviour system for hardware communication using FreeRTOS, enabling reliable real-time task scheduling and inter-component messaging. Updated a fundamental library stack shared across 10+ projects, improving code reuse and maintainability across the Dyson embedded ecosystem."
      ],
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    {
      "name": "AI4EO & CNN Research",
      "organisation": "University of Leicester",
      "start": null,
      "end": null,
      "summary": [
        "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."
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        "Classified high-resolution satellite images for forest fire detection using CNNs, Random Forest, and Support Vector Machines. The research applied AI for Earth Observation (AI4EO), using deep learning to identify fire-affected regions from multispectral satellite imagery with high accuracy.",
        "Evaluated multiple CNN architectures for autonomous vehicle applications, benchmarking performance across detection accuracy, inference speed, and memory footprint. Optimised models with Transfer Learning and TensorRT to achieve real-time inference suitable for deployment on vehicle-mounted compute platforms."
      ],
      "stack": [
        "CNNs",
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        "TensorFlow",
        "TensorRT",
        "Transfer Learning",
        "Remote Sensing"
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      "qualification": "MS in Artificial Intelligence",
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      "start": "2025-01",
      "end": "2026-12",
      "result": null,
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      "qualification": "MS in Embedded Systems and Control Engineering",
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      "location": "England",
      "start": "2021-01",
      "end": "2022-08",
      "result": "Distinction",
      "thesis": "Performance Evaluation of Deep Learning Techniques for Object Detection in Autonomous Vehicles"
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      "qualification": "BTech in Electronics and Communication Engineering",
      "institution": "Jawaharlal Nehru Technological University",
      "institutionUrl": "https://jntuh.ac.in",
      "location": "India",
      "start": "2016-08",
      "end": "2020-10",
      "result": "First Class",
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    "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."
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      "type": "Book",
      "title": "How to Make Your Model Fast",
      "subtitle": "A Systems View of Efficient Machine Learning, from Silicon to Agents",
      "url": "https://ai.usamah.me",
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      "question": "Who is Usamah Zaheer?",
      "answer": "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."
    },
    {
      "question": "What does Usamah Zaheer work on?",
      "answer": "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."
    },
    {
      "question": "Where did Usamah Zaheer study?",
      "answer": "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."
    },
    {
      "question": "Where has Usamah Zaheer worked?",
      "answer": "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."
    },
    {
      "question": "What book has Usamah Zaheer written?",
      "answer": "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."
    },
    {
      "question": "What does Usamah Zaheer write about?",
      "answer": "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."
    },
    {
      "question": "How can I contact Usamah Zaheer?",
      "answer": "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."
    }
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