Deep learning engineer job description.
Hiring a deep learning engineer or building on AI expertise? This job description outlines work with neural networks, model architecture, and performance tuning. It also details the technical knowledge required and what professionals in this space typically earn.
What does a deep learning engineer do?
A deep learning engineer builds neural networks to solve complex problems in fields like computer vision, speech recognition, natural language processing, or autonomous systems. They develop and optimise architectures such as CNNs, RNNs, or transformers.
Responsibilities include curating large datasets, tuning hyperparameters, training deep models on GPUs or TPUs, and evaluating performance across benchmarks. They frequently work with frameworks like PyTorch, TensorFlow, and Keras.
In research-heavy teams, they contribute to cutting-edge model development. In product-focused businesses, they embed these models into scalable services or edge applications with clear commercial value.
Key responsibilities of a deep learning engineer.
Deep learning engineers specialise in building neural networks for complex tasks. Responsibilities include:
-
Developing CNNs, RNNs, transformers, and other deep learning architectures
-
Training models on GPU/TPU clusters with large datasets
-
Tuning hyperparameters to optimise accuracy and performance
-
Implementing loss functions, activation layers, and gradient optimisers
-
Deploying models using TensorFlow Serving or ONNX runtime
-
Collaborating with product and data teams to scope model goals
-
Supporting applications in vision, NLP, speech, or reinforcement learning
-
Logging experiments and maintaining model version control
-
Conducting peer reviews and sharing research insights
-
Ensuring models are efficient, stable, and production-ready
This role blends cutting-edge model development with scalable engineering.
Skills and requirements for a deep learning engineer.
Deep learning engineers develop advanced neural networks. Employers typically look for:
-
4–7 years of experience in deep learning or AI engineering
-
Strong Python skills with TensorFlow, PyTorch, or JAX
-
Expertise in CNNs, RNNs, transformers, and other deep models
-
Ability to train, tune, and evaluate models at scale
-
Familiarity with large datasets, GPU acceleration, and parallel computing
-
Experience deploying models into production
-
Knowledge of interpretability, performance, and drift monitoring
-
Skilled in debugging and improving training workflows
-
Portfolio of applied projects (NLP, vision, time-series)
Most deep learning engineers focus on innovative, research-intensive projects.
Average salary for a deep learning engineer.
In the UK, the average salary for a deep learning engineer typically ranges from £55,000 to £90,000, depending on model complexity, compute optimisation, and research experience.
-
Mid-level engineers earn between £55,000 and £72,000
-
Senior engineers training large neural networks and working on vision or NLP may earn between £73,000 and £90,000
-
Academic background combined with applied industry success is highly valued
Top salaries are found in autonomous vehicles, biotech, and advanced AI labs.
Career progression for a deep learning engineer.
A deep learning engineer specialises in neural networks and architectures such as CNNs, RNNs, and transformers. This is a high-skill technical role that drives innovation in AI products. A typical path includes:
ML engineer / AI researcher
Supports neural network development, model training, and dataset preparation.
Deep learning engineer
Designs and implements deep learning models for use in image, speech, or language applications.
Senior deep learning engineer
Leads optimisation for inference, model deployment, and high-performance training.
DL architect
Shapes strategy for deep learning infrastructure and contributes to model innovation.
Head of AI
Leads the application of deep learning across the organisation’s product suite or R&D team.
salary guide
Our UK data salary guide.
Deep learning engineers work with neural networks, computer vision, and NLP systems. Compensation should reflect expertise across frameworks and datasets.
Our UK data salary guide includes salary benchmarks, 2024 hiring comparisons, and forward-looking projections through 2026.
FAQS
Deep learning engineer FAQs.
Primarily in medtech, autonomous vehicles, finance (for algo trading or fraud detection), and synthetic media. Most commercial roles are focused on computer vision, NLP, or speech recognition — often where custom models outperform off-the-shelf APIs.
Not anymore. While research labs may require advanced degrees, many product-focused companies prioritise hands-on experience with TensorFlow, PyTorch, and real-world datasets over academic credentials.
Depth and complexity. Deep learning engineers deal with architectures (e.g. CNNs, RNNs, Transformers), manage large GPU workloads, and often contribute to model design — not just tuning.
Hiring for hype rather than application. Businesses often bring in deep learning talent without clear use cases, leading to underutilised expertise. Be sure there's a real need for custom models and data scale.
Many become AI architects, research leads, or move into startup leadership within deep tech. Others transition into advanced platform optimisation or edge inference roles.
