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Deep Learning Engineer Jobs

Engineers who design, implement, and optimise neural networks for a wide range of applications. A critical role in the AI ecosystem, driving innovation and solving complex problems.

Open roles
4
Salary range
221k – 507k
Hiring companies
2
View 4 live roles → Get an alert

Deep Learning Engineers are at the forefront of artificial intelligence, specialising in the design and implementation of neural networks. These engineers work on a variety of applications, from computer vision and natural language processing to recommendation systems and autonomous vehicles. They are hired by a range of organisations, from research-heavy startups to large tech companies and academic institutions, where they collaborate with data scientists, researchers, and software engineers to develop cutting-edge AI solutions.

What the role does

Inside the role of a Deep Learning Engineer

A typical week for a Deep Learning Engineer is a mix of research, development, and collaboration. They spend time coding, experimenting with new models, and refining existing ones.

  1. 01
    Design and implement neural network architectures.
  2. 02
    Train and test models using large datasets.
  3. 03
    Optimise model performance and efficiency.
  4. 04
    Collaborate with cross-functional teams to integrate AI solutions.
  5. 05
    Document and present findings to stakeholders.
  6. 06
    Stay updated with the latest research and industry trends.
Salary on the board

221k – 507k

Based on advertised midpoints across the 7 priced listings posted in the last 12 months. Base salary only.

Salary visibility
80% of listings advertise a salary.
By seniority
k base
Senior
221
507
7 jobs
Skills & tools

What hiring managers ask for

% of 3 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Deep Learning
100%
Python
100%
PyTorch
100%
TensorRT
100%
TensorRT-LLM
100%
vLLM
100%
SGLang
100%
CUDA
100%
Docker
100%
Triton Inference Server
100%
Diffusion Models
100%
Career ladder

From Junior to Principal

A typical UK progression for deep learning engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.

  1. Level 1

    Junior Deep Learning Engineer

    0–2 yrs

    Assist in the development and testing of neural networks, focusing on learning and contributing to team projects.

  2. Level 2

    Deep Learning Engineer

    2–5 yrs

    Take ownership of specific projects, design and implement neural networks, and contribute to the overall architecture of AI systems.

  3. Level 3

    Senior Deep Learning Engineer

    5–8 yrs

    Lead the development of complex AI systems, mentor junior engineers, and drive innovation within the team.

  4. Level 4

    Principal Deep Learning Engineer

    8+ yrs

    Strategise and oversee the technical direction of AI projects, influence company-wide AI initiatives, and collaborate with senior leadership.

Pathway

How to become a Deep Learning Engineer

There's no single route, but most people follow some version of these steps.

  1. 1

    Acquire foundational skills

    Gain a strong understanding of mathematics, programming, and machine learning fundamentals. Build a portfolio of projects to demonstrate your skills.

  2. 2

    Specialise in deep learning

    Focus on advanced topics in deep learning, such as neural network architectures, optimisation techniques, and practical applications.

  3. 3

    Gain industry experience

    Work on real-world projects, either through internships or entry-level positions, to apply your knowledge and gain practical experience.

  4. 4

    Develop leadership skills

    Take on more responsibility, lead projects, and mentor junior engineers. Develop your ability to communicate complex ideas to non-technical stakeholders.

  5. 5

    Influence strategic decisions

    Contribute to the strategic direction of AI projects and initiatives within your organisation. Collaborate with senior leadership to drive innovation.

  6. 6

    Become a thought leader

    Publish research, speak at conferences, and contribute to the broader AI community. Establish yourself as an expert in the field.

Live jobs

4 live roles

NVIDIA logo

Senior Deep Learning Software Engineer, Inference

Design and optimize high-performance deep learning inference software for NVIDIA's GPU-accelerated platforms, focusing on frameworks like vLLM and SGLang. Improve model serving efficiency for large language and generative AI models across datacenter and edge devices. Collaborate with research and engineering teams to implement cutting-edge algorithms and performance optimizations using CUDA, Triton, and other low-level tools.

NVIDIA PLN 221,250 – PLN 507,000 pa
Remote Permanent
NVIDIA logo

Senior Deep Learning Software Engineer, Inference

This role involves designing, optimizing, and maintaining high-performance deep learning inference software for large-scale language and generative AI models. The engineer will work on GPU-accelerated frameworks like vLLM, SGLang, and FlashInfer, implementing cutting-edge algorithms and performance improvements across NVIDIA's full range of accelerators. Collaboration with open-source communities and cross-team partners is central to advancing model serving efficiency and deployment at scale.

NVIDIA PLN 221,250 – PLN 507,000 pa

Senior Computer Vision Engineer - Deep Learning

Design and deploy production-grade computer vision systems using deep learning techniques such as CNNs and Vision Transformers, translating research into robust AI solutions for defence and security applications. Work across the full development lifecycle, from model design and training to deployment on cloud and edge platforms. Collaborate with researchers, engineers, and customers in a technically challenging environment focused on real-world autonomy and perception systems.

MFK Recruitment Brentford, London, TW8 9DE, United Kingdom £70,000 – £95,000 pa
Hybrid Permanent Clearance Required
NVIDIA logo

Senior Deep Learning Software Engineer, Inference

Design and optimize GPU-accelerated deep learning inference software for large-scale language and generative AI models. Contribute to open-source frameworks like vLLM and SGLang, and implement performance improvements across NVIDIA's full range of accelerators from datacenter to edge. Work closely with research and engineering teams to profile, tune, and deploy state-of-the-art model serving solutions using CUDA, Triton, and other low-level optimization tools.

NVIDIA Netherlands
FAQs

Common questions

  • Python is the most widely used language in deep learning, thanks to its extensive libraries and frameworks like TensorFlow and PyTorch. Familiarity with C++ and CUDA can also be beneficial for performance optimisation.

  • Key skills include a strong foundation in mathematics (linear algebra, calculus, statistics), proficiency in programming, knowledge of machine learning algorithms, and experience with deep learning frameworks.

  • Deep learning engineers are in demand across various industries, including tech, healthcare, finance, automotive, and academia. Research-heavy startups, scaleups, and large tech companies are common employers.

  • The typical progression starts with a junior role, advancing to a senior position, and eventually leading to principal or lead roles. Each step involves increasing responsibility and influence over AI projects and initiatives.

  • For detailed salary information, please refer to the salary section on this page, which is updated with the latest data from live job listings.

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