Free Entry-level deep learning engineer CV (2026)

Follow the sections to build a CV that reads clearly for hiring managers and technical leads in the UK. Each section explains what to include and gives concise examples you can adapt for your own document.

Put your full name, UK role label, city and postcode, mobile number, email, and one link to a portfolio or GitHub at the top of the CV. Include a short custom URL to a project portfolio or readable GitHub profile so reviewers can click to your code and model demos.

Mention your right-to-work status and any visa eligibility if applicable to UK roles.

Choose a 1-2 sentence summary if you have internships or research experience, or a 1-2 sentence objective if you are shifting from another field. Use active language to state your focus, for example neural networks, computer vision, or NLP, and mention a clear result such as model accuracy improvement or a deployed demo.

List your degree, university, graduation date or expected date, and relevant coursework that maps to the job description such as machine learning, probability, linear algebra, and software engineering. In the UK, reference GCSEs and A-levels where helpful, note UCAS status if relevant, and highlight if you attended a Russell Group university.

Add a short note for a dissertation, capstone, or independent study that involved model training or data preprocessing, and include any measurable outcomes like dataset size or performance metric.

Include a concise section listing core technical skills (programming languages, ML frameworks, data processing tools, cloud platforms) and common tools used in ML workflows. Distinguish between languages (Python, C++), libraries (PyTorch, TensorFlow, scikit-learn), data handling (pandas, NumPy), cloud and MLOps (AWS, GCP, Docker, Git, MLflow), and operating systems (Linux).

Mention any UK-specific certifications or courses if relevant.

Feature 3 to 5 projects that show end-to-end work, for example data collection, model training, evaluation, and deployment. For each project use 2-3 bullet lines that state the problem, your technical approach, and the measurable outcome such as accuracy, latency, or user engagement.

Include details on datasets, model architecture, and the exact commit used for results via a GitHub README. Where applicable, note UK data sources or UK-specific constraints.

Describe internships and research roles with 2-3 concise bullets each, focusing on your contribution to models, datasets, or tools. Where possible quantify results, for example improved validation accuracy by X points, reduced inference time to Y ms, or created a tool used by teammates.

When applying to healthcare roles, reference NHS pay bands if appropriate and keep other entries focused on deliverables and impact.

Add optional sections for References, Certifications, or Training relevant to the UK market. If you have professional registrations or memberships (eg.

IEEE, UK AI mentoring programs), include them here. Consider including a short note about eligibility to work in the UK and preferred locations within the country.