Deep Learning Engineer

Key Responsibilities:

• Design, train, validate, and deploy deep learning models using frameworks such as PyTorch or TensorFlow.
• Collaborate with data scientists, MLOps engineers, and product teams to integrate models into scalable pipelines or production systems.
• Perform model optimization (e.g., pruning, quantization) to improve latency and efficiency.
• Conduct literature reviews and experiments to evaluate the latest advances in deep learning.
• Develop reproducible experiments and maintain well-documented code repositories.
• Contribute to architecture design for AI solutions and advise on best practices in model lifecycle management.
• Participate in code reviews and knowledge-sharing sessions.

Required Skills & Experience:

• 3+ years of experience in deep learning or applied machine learning roles.
• Strong proficiency in Python and libraries like PyTorch, TensorFlow, Keras, or JAX.
• Hands-on experience with model deployment, containerization (Docker), and serving (TorchServe, TensorFlow Serving, or ONNX).
• Solid understanding of deep learning architectures such as CNNs, RNNs, Transformers, etc.
• Experience with data preprocessing, augmentation, and handling large datasets (image, text, or structured).
• Familiarity with MLOps tools such as MLflow, Weights & Biases, or DVC.
• Ability to work independently and manage project timelines and deliverables.

Preferred Qualifications:

• MSc or PhD in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Experience with cloud platforms (AWS, GCP, Azure) and GPU-based training.
• Knowledge of reinforcement learning or generative models (e.g., GANs, VAEs) is a plus.
• Prior experience working in regulated environments (e.g., medical imaging, finance) is an advantage.
• Based in or willing to commute regularly to Amsterdam.