Deep learning engineer job description
What does a deep learning engineer do ?
Deep learning engineer job description A deep learning engineer builds neural networks to solve complex problems in field like computer vision, speech recognition, natural language processing, or autonomous systems, They develop and optimize architectures such as CNNs, RNNs, or transformers deep learning engineer job description
deep learning engineer job description Responsibilities including 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 Kera’s
In search 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 specialize 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 optimize accuracy and performance
- Implementing loss functions, activations layers, and gradient optimizers
- Deploying models using TensorFlow serving or ONMX Runtime
- Collaborating with product and data teams to scope models goals
- Supporting applications in vision, NLP, Speech, or reinforcement learning
- Logging experiments and maintain 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 a 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 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 optimization, 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 engineers
- A deep learning engineers specializes in neural networks and architectures such as CNNs, RNNs, and transformers, This is high skills technical role that drives innovation in AI products, A typical path include
ML Engineer / AI researcher
- Supports neural network development, model training, and datasets preparation
Deep learning Engineer
- Design and Implements deep learning models for use in image, speech, or language applications
Senior deep learning engineer
- Leads optimization 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 organizations product suite or R&D team
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