Deep Learning Engineer Jobs

Specialists who design, train, and deploy deep neural networks. A core role in the machine learning ecosystem, driving innovation in AI applications.

Open roles
4
Hiring companies
3

Deep Learning Engineers are at the forefront of AI innovation, focusing on the design, training, and deployment of deep neural networks. These engineers work across a variety of sectors, from tech giants and scaleups to research-heavy startups and the larger consultancies. Their role is crucial in developing cutting-edge solutions for tasks such as image and speech recognition, natural language processing, and autonomous systems.

What the role does

Inside the role of a Deep Learning Engineer

A typical week for a Deep Learning Engineer is a mix of model development, experimentation, and collaboration with cross-functional teams.

  1. 01
    Design and implement deep neural network architectures.
  2. 02
    Train models using large datasets and optimise performance.
  3. 03
    Collaborate with data scientists and software engineers.
  4. 04
    Conduct experiments to validate model accuracy and efficiency.
  5. 05
    Document findings and present results to stakeholders.
  6. 06
    Stay updated with the latest research and industry trends.
Skills & tools

What hiring managers ask for

% of 7 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Deep Learning
100%
Python
86%
PyTorch
71%
TensorRT
57%
CUDA
57%
Docker
57%
Triton Inference Server
57%
Neural Networks
43%
TensorRT-LLM
29%
vLLM
29%
SGLang
29%
Diffusion Models
29%
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 deep learning models, with a focus on learning and contributing to team projects.

  2. Level 2

    Deep Learning Engineer

    2–5 yrs

    Own the design and implementation of deep learning models, working on complex problems and leading small projects.

  3. Level 3

    Senior Deep Learning Engineer

    5–8 yrs

    Lead the development of advanced deep learning solutions, 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 deep learning initiatives, influence company-wide AI strategy, and lead large-scale projects.

Pathway

How to become a Deep Learning Engineer

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

  1. 1

    Foundational Skills

    Gain a strong understanding of machine learning fundamentals and programming languages like Python and TensorFlow.

  2. 2

    Specialisation in Deep Learning

    Focus on deep learning techniques, including neural network architectures, training methodologies, and optimisation strategies.

  3. 3

    Practical Experience

    Apply deep learning knowledge in real-world projects, working on datasets and models to solve specific problems.

  4. 4

    Advanced Techniques

    Explore advanced topics such as reinforcement learning, generative models, and transfer learning to enhance your expertise.

  5. 5

    Leadership and Mentorship

    Take on leadership roles, mentor junior engineers, and contribute to the strategic direction of deep learning initiatives.

  6. 6

    Innovation and Research

    Drive innovation by conducting original research, publishing papers, and contributing to the broader AI community.

Live jobs

4 live roles

Humanoid · 11–50 people · Series A

Senior Deep Learning Engineer, Whole-Body Control

  • London
  • On-site
  • Senior
  • Permanent

Develops learning-based locomotion and whole-body control policies for humanoid robots, using reinforcement and imitation learning, generative models and motion primitives. Builds scalable simulation and training pipelines in Isaac Lab and MuJoCo, and deploys, debugs and fine-tunes policies on physical robots, tackling sim-to-real transfer and real-time inference latency.

Isaac Lab · Isaac Sim · MuJoCo +1

Last month

Latent Edge Ltd

Robotics Learning Engineer – Humanoid Robotics / Deep Learning

  • London
  • On-site
  • £150,000 – £200,000 pa
  • Mid-level
  • Permanent

Develops and deploys robot manipulation policies using behaviour cloning and reinforcement learning, training and evaluating models on real humanoid hardware. Works within a specialist Manipulation team alongside simulation, teleoperation and controls engineers to improve sim-to-real transfer and robot performance.

PyTorch · JAX · OpenVLA +1

2 days ago

eFinancialCareers

Deep Learning Researchers / Engineers- Sophisticated Prop Trading Firm

  • London
  • On-site
  • Mid-level
  • Permanent

Build a greenfield deep learning research stack for a centralised trading book, working in pod-like teams with shared capital and strategic influence. Focus is deep-learning-first: neural networks, transformers, LLM-driven research, multi-modal modelling, NLP and agentic/autonomous research systems, including LLM fine-tuning for regression and forecasting.

GPUs

Yesterday

MFK Recruitment

Senior Computer Vision Engineer - Deep Learning

  • Brentford
  • Hybrid
  • £70,000 – £95,000 pa
  • Senior
  • Permanent

Hands-on role designing and deploying production computer vision systems using CNNs and Vision Transformers, from model architecture and training pipelines to optimisation for cloud and edge. Works alongside software engineers, researchers and customers, translating recent AI research into reliable products for defence and security environments.

PyTorch · TensorFlow · Git +6

SC clearance 2 weeks ago
Hiring locations

Where this role is hiring

The locations with the most live listings for this role today.

FAQs

Common questions

  • Essential skills include a strong foundation in mathematics, programming proficiency in Python, and expertise in deep learning frameworks like TensorFlow and PyTorch.

  • Gain experience in machine learning, complete relevant courses or certifications, and work on personal or open-source projects to build a portfolio.

  • Common industries include tech, healthcare, finance, and automotive, where deep learning is used for tasks like image recognition, natural language processing, and predictive analytics.

  • Salaries vary based on experience and location. For more detailed salary information, refer to the salary section on this page.

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