Deep learning engineer CV: Free Example (2026)

Read the sample sections first and then paste the template into your preferred editor. Replace the sample projects and metrics with your own details and keep the layout compact for one page if you have under 6 years of UK experience; use two pages for longer careers.

Focus on concrete outcomes, the models or systems you built, and the tools you used so a recruiter or hiring manager in the UK can quickly assess your fit. If you are applying to NHS Digital or other regulated environments, emphasise governance, data protection, and clinical relevance where appropriate.

When distributing your CV, reference UK job boards such as Reed, Indeed UK, Totaljobs, and LinkedIn.

Write a concise headline and two to three sentence CV summary that highlights your role, domain, and strengths. For example, "Deep Learning Engineer with 4 years building computer vision models for retail analytics, experienced in PyTorch, model optimisation, and deploying models to cloud inference services." Include your phone number, email, LinkedIn profile, and a GitHub or portfolio link so a recruiter can quickly view your code or demos.

Keep the summary outcome‑focused and avoid vague phrases without metrics or context. If you have right‑to‑work in the UK, indicate your status in the contact area.

For each role list the employer, location (city, country), dates (DD/MM/YYYY), and your job title, then add three to five concise bullet points showing impact. Lead with the problem, then your action and the measurable result, for example, "Reduced inference latency 3x by converting model to TensorRT and pruning unused weights, enabling real‑time processing on edge devices." Use past tense for previous roles and present tense for your current role, and place the most relevant points first.

Tailor these bullets to the job description by mirroring important keywords such as model types, frameworks, and deployment platforms. If applying to NHS or other healthcare organisations, mention any regulatory compliance or data governance work.

Describe key projects with a one‑line title, tech stack, and two to three sentence summary that includes your role and the outcome. For example, "Traffic sign recognition, PyTorch, deployed to AWS Lambda, achieved 97% accuracy on validation set and reduced false positives by 40% through data augmentation and focal loss." Include links to your code or demos and ensure results are quantified (accuracy, latency, throughput, or business impact).

Keep content concise and readable for applicant tracking systems (ATS).

In the UK, list academic qualifications in reverse chronological order. Include GCSEs and A‑levels where relevant, UCAS points if applicable, degrees (e.g.

BSc, MSc, PhD) with institution and dates (DD/MM/YYYY), and the degree classification. Mention whether you studied at a Russell Group university if applicable.

For international degrees, provide the awarding body and any accreditation. If relevant, include professional certifications and memberships.

This section should align with UK standards and terminology.

If you require sponsorship or have a specific visa status, state it clearly in the header or summary. In the UK job market, many employers request confirmation of the right to work in the UK, so be explicit to avoid delays.