À propos de ce poste Senior Machine Learning Specialist chez Hamster
You will be responsible for developing and implementing machine learning solutions that power our AI-native platform. This role requires expertise in custom model development, transformer architectures, and production deployment of ML systems.
Responsibilities
Design and develop custom machine learning models for specific business use cases.
Implement and optimize transformer architectures for natural language processing tasks.
Develop and maintain ML pipelines for data preprocessing, model training, and inference.
Work with large language models and implement fine-tuning strategies.
Implement retrieval-augmented generation (RAG) systems and optimize their performance.
Collaborate with engineering teams to deploy ML models in production environments.
Monitor and maintain model performance in production, implementing retraining strategies as needed.
Conduct research on emerging ML techniques and evaluate their applicability to our platform.
Mentor junior ML engineers and contribute to best practices within the team.
Required Skills and Qualifications
Master's degree or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
5+ years of experience in machine learning development and deployment.
Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, or similar).
Deep understanding of transformer architectures and their applications in NLP.
Experience with large language models and fine-tuning techniques.
Proficiency in implementing and optimizing RAG systems.
Experience with ML model deployment and production monitoring.
Strong understanding of data preprocessing, feature engineering, and model evaluation techniques.
Experience with cloud platforms and ML infrastructure (AWS SageMaker, Google Vertex AI, or similar).
Excellent problem-solving skills and attention to detail.
Strong communication and interpersonal skills.
Preferred Qualifications
Experience with MLOps and ML pipeline orchestration tools.
Knowledge of distributed training and model optimization techniques.
Experience with vector databases and similarity search algorithms.
Familiarity with reinforcement learning and multi-agent systems.
