Deep Learning Engineer

Responsibilities

• Design and implement state-of-the-art deep learning architectures (CNNs, RNNs, Transformers, etc.) tailored to project needs.
• Develop scalable and efficient training pipelines using large-scale datasets.
• Experiment with novel techniques to improve model accuracy, robustness, and generalization.
• Collaborate with data scientists, engineers, and domain experts to integrate models into production systems.
• Optimize models for deployment on cloud or edge platforms.
• Conduct thorough evaluation and validation of models with appropriate metrics.
• Document models, experiments, and codebase for knowledge sharing and reproducibility.
• Stay up-to-date with the latest research papers, tools, and frameworks in deep learning.

Requirements

• Proven experience (3+ years) in developing and deploying deep learning models.
• Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Hands-on experience with computer vision, NLP, or other relevant domains.
• Familiarity with data preprocessing, augmentation, and handling large datasets.
• Experience with cloud platforms (AWS, GCP, Azure) or containerization (Docker, Kubernetes) is a plus.
• Knowledge of optimization techniques, hyperparameter tuning, and model compression.
• Ability to write clean, maintainable, and well-documented code.
• Excellent problem-solving skills and a proactive approach.
• Strong communication skills in English; Swedish language skills are a plus but not mandatory.
• Ability to work independently and deliver on agreed deadlines.

Preferred Qualifications

• MSc or PhD in Computer Science, Machine Learning, AI, or related fields.
• Experience with reinforcement learning or generative models.
• Background in deploying AI models in production environments.
• Prior freelance or consulting experience.
• Knowledge of software development best practices including version control (Git).