You are an experienced AI Developer with a strong focus on machine learning model training, tuning, and optimization. You will be a key contributor to the AI development team in a government-led digital transformation project. The role involves developing, testing, and optimizing high-performing models for legal and administrative use cases.
We are an AI startup that empowers developers to build applications that use natural language as the interface to data. Our open-source framework Haystack is used by thousands of developers to build LLM applications. deepset AI Platform (formerly known as deepset Cloud) enables enterprises worldwide to unlock the potential of large language models for their business cases. Come join our team and work with tech experts on real-world cases, in a fast-paced and high-impact environment, and shape the AI space together with us.
At deepset you will find an environment with equal opportunities to grow, share ideas, build relationships, and succeed. We believe every employee is important and generates a direct impact on our team’s success.
We are certified by Flexa® Careers as a flexible employer, and our remote first culture is designed to provide work-life balance, autonomy, and flexibility. Our daily meetings, the in person "re:base", and the social events allow us to work with great synergy. We would like to invite you to visit our life page to learn more about our culture.
Train, fine-tune, and evaluate machine learning models with a focus on legal and administrative datasets
Design and implement optimization strategies to improve model performance and generalizability
Conduct experiments to compare models, hyperparameters, and techniques
Collaborate with the AI Architect and MLOps Engineers on model deployment and monitoring
Document model performance, tuning parameters, and experiment outcomes for traceability
Degree in Computer Science, Data Science, AI, or related field
Minimum of 2 years of professional experience in machine learning model development
Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
Experience with model evaluation techniques and performance metrics
Knowledge of regularization, cross-validation, hyperparameter tuning, and model selection
Understanding of the ML lifecycle and integration with MLOps tools
Preferred Experience
Experience in legal tech, administrative processes, or public sector ML use cases
Familiarity with NLP models (e.g., transformers, LLMs) and structured/unstructured data handling