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AI Engineer with 5+ years in ML, real-time systems & probabilistic modelling.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Machine learning (ML) research engineer with a PhD in Physics, specialising in in ML for low-latency, real-time decision-making. Experienced in delivering ML systems at scale, with strong expertise in statistics and probabilistic modelling. Focused on driving real-world impact through ML applications.
University of Oxford
PhD Particle Physics · Particle Physics
August 1, 2019 – June 30, 2024
University of Manchester
Master's in Physics (MPhys) · Physics
August 1, 2015 – June 30, 2019
European Organization for Nuclear Research (CERN)
CERN Research Fellow
January 1, 2024 – Present
Switzerland
Keble College, University of Oxford
Tutor for Undergraduate Physics
January 1, 2020 – January 1, 2023
United Kingdom
University of Oxford
Doctoral Researcher (DPhil Particle Physics)
January 1, 2019 – January 1, 2024
United Kingdom
University of Manchester
Master's Project Researcher
January 1, 2018 – January 1, 2019
United Kingdom
University of Manchester
Detector Hardware Research Internship
January 1, 2018 – January 1, 2018
United Kingdom
Teach First
Teach First Insight Program
January 1, 2017 – January 1, 2017
United Kingdom
Published ML Anomaly Detection System in White Paper
Unknown
January 1, 2026 – Present
DPhil Thesis Published by Springer Nature
Springer Nature
January 1, 2026 – Present
Springer Thesis Award
Springer Nature
January 1, 2025 – Present
Presented ML Anomaly Detection System at NPML Conference
NPML Conference
January 1, 2025 – Present
Conference Proceedings on High-Dimensional Data Analysis
NuFact 2024
January 1, 2024 – Present
Best Poster in Accelerator Neutrino Track
Neutrino 2022 Conference
January 1, 2022 – Present
Kingsgate/Briggs-Myers/STFC scholarship
Science and Technology Facilities Council (STFC) and Lincoln College, Oxford
January 1, 2019 – January 1, 2024
Best Research Intern Presentation
University of Manchester Physics Department
January 1, 2018 – Present
Cultural Fit Analysis
The candidate's background in academic research, particularly at CERN and Oxford, suggests a strong cultural fit for environments that value innovation, rigorous scientific methodology, and collaborative problem-solving. Their experience leading teams and driving international collaborations indicates an ability to work effectively in diverse, high-performing teams. The focus on real-world impact through ML applications aligns well with product-driven AI roles. The breadth of skills from low-level C++ optimization to high-level ML framework usage shows adaptability and a continuous learning mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership through leading a research team and mentoring junior researchers. Their experience in international collaborations and presenting complex findings indicates excellent communication and teamwork skills. The ability to design and deploy real-time ML systems in a production environment, coupled with rigorous statistical testing, suggests a detail-oriented and robust operational approach. Their background in physics and research implies a strong aptitude for problem-solving and critical thinking, which are highly valuable in an AI engineering role.