Professional Summary
I am an aspiring hardware systems engineer and kernel programmer with a deep interest in processor architecture, memory management, and low-level performance tuning.
My 11 years of hands-on experience at Apple's Genius Bar provides me with a strong foundation in complex technical troubleshooting and a unique understanding of systems from the user’s perspective.
With proficiencies in C, Python, and WebGPU, I am driven to build efficient, foundational systems that power applications. I have demonstrated a strong commitment to learning through my recent self-directed qualifications in Computer Science, Embedded C, and Machine Learning, fuelled by my belief you can’t learn less.
Work Experience
Apple Genius 2014 - Present
Role Requirements
- Provides frontline technical support at the Genius Bar for all Apple products & services
- Alleviates customer concerns, tackling emotional situations with empathy
- Efficiently finds solutions to deliver 3-5 appointments per hour
- Physically repairs a wide range of Apple hardware from Intel to Apple Silicon
- Provides mentorship and technical assistance to team members
- Uses clear language to explain technology to an audience of varying skillset
Highlights
- Genius Training at Stockley Park, London
- Being part of historic product launches such as Apple Watch, iPhone 6+ and Vision Pro
- Identifiying a fault in a repair part, leading to an Apple Engineering redesign.
- Implementing a store-wide network solution for 10x faster downloads.
- Being a part of Apple Touchwood's New Store Opening and cabling the Repair Room
Skills
- Technical Support
- Problem Solving
- Customer Service
- Mentoring
- Nimble Learning
- Teamwork
Apple Store Training Career ExperienceJan – Jun 2024
Role Requirements
- Facilitated Apple’s training content for 80+ Apple Store team members
- Self managed and planned time effectively to work training into existing store schedules
- Partnered closely with management to identify specific learning opportunities.
- Delivered new employee training to instil and teach Apple's values and technical process
Highlights
- Self-teaching advanced Numbers formula for effective data reporting
- Listening to and understanding the learning challenges of all team members
- Learning JavaScript for Automation to script team member's training reports
Skills
- Time Management
- Planning
- JavaScript
- Training Facilitation
- Spreadsheets
- Public Speaking
Projects
Snowflake - A Python User Interface Tool Powered by WebGPU
Developing an efficient user interface framework in Python using WebGPU with shader driven animation, high-volume instance rendering (tens of thousands of elements per frame), and efficient buffer and memory management reducing the python call frame. It harnesses the power of native GPU with the ease and comfort of writing in Python.
Key features:
- Intuative OOP structure for convienient scene mapping and native python buffer updates.
- Custom compute/render pipelines via wgpu-py for performant animation and rendering
- Magic method interpreter with a 16 byte opcode buffer bypassing AST node inspection
- Direct bytearray’s to buffer, preventing third party libraries adding extra Python call frames
- Ping-pong buffering for optimal performance and no race conditions
- In Progress: Stable NDC SDF Font Rendering System for a unified coordinate system
Project Skills
- WebGPU
- Graphics Shaders
- Render Pipelines
- Buffer Alignment
- Parallelism
- Systems Design
PDF-to-eBook Conversion with Machine Learning Optimisation
After purchasing a Kindle for reading academic papers I am developing a script that converts PDF's into eBook-friendly formats, with a focus on accurate text and style extraction, machine learning k means clustering and LLM cleanup resulting in context aware, well structured outputs. It targets scientific papers as they are often scanned, mathmatical documents where conventional converters fail.
Key features:
- Automatic scanned PDF OCR detection and text handling for wider document support
- Reimagined k-prototypes clustering of document blocks using mixed numerical and categorical features for optimised text and style grouping.
- SIMD-Accelerated C Extension: Leveraged Apple Silicon NEON to accelerate categorical dissimilarity, achieving 1.8–3.4× speedup over the native k-means library.
- Thread-Safe Parallelism: Integrated pthread-based thread-local storage and mutexes for safe concurrent SIMD execution.
- In Progres: Dissasembly of K Prototypes grouping into a correct style placement
Project Skills
- PyMyPDF
- Numpy
- K Means
- C Python Extensions
- SIMD Instruction
- Machine Learning
Education
| Certificates - Click name to view | Formal Education |
|---|---|
| Computer Science - Professional Certificate | Bath Spa University |
| Intro to PyTorch and Neural Networks | Commercial Music - BA (Hons) 2:2 |
| Embedded Systems Using C | Bishop Vesey’s Grammar School |
| ARM Cortex: STM32 Mastering Embedded Systems | A2, AS and GCSE |
| Maths, Physics, Music & Art | |
| Biology, Chemistry, French, | |
| English Lit / Lang, General Studies |
Additional Skills
Technical
- ACPI
- Objective-C
- DriverKit
- Embedded C
- Nuvoton Datasheets
- VS Code
- Flask
- HTML
- CSS
- STM32CubeIDE
- Command Line
- Logic Pro
- Final Cut
- Capture One
Personal
- Photography
- Woodworking
- Music Performance
- Grade 8 Distinction Guitarist
- Classical Pianist
- Travel