Senior Python AI Engineer (Remote) - LLM & RLHF

Hired; Bologna; Remoto; EUR 79.000 - 132.000; Tempo pieno

Descrizione del lavoro

Hired is partnering with a global AI leader to recruit a Senior Python Engineer for remote work. The role focuses on designing and optimizing Python-based pipelines to train and evaluate large language models, with emphasis on data generation, benchmarking, and model alignment.

You will collaborate with researchers to implement supervised fine-tuning and RLHF, contributing to high-quality datasets and reward models for continued model improvement.

Competenze

• Proficiency in Python and related frameworks/libraries for AI/ML tasks.
• Experience with supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
• Strong understanding of evaluation strategies and benchmarking processes for AI models.
• Ability to design and implement Python code for data generation and model optimization.
• Familiarity with AI model response evaluation and ranking methodologies.

Mansioni

• Design, develop, and maintain efficient, high-quality Python code to train and optimize AI models.
• Conduct evaluations to benchmark model performance and analyze results for continuous improvement.
• Evaluate and rank AI model responses to user queries across diverse domains, ensuring alignment with predefined criteria.
• Lead efforts in supervised fine-tuning, including creating and maintaining high-quality, task-specific datasets.
• Collaborate with researchers and annotators to execute reinforcement learning with human feedback and refine reward models.

Conoscenze

Python

SFT

RLHF

Model evaluation

Data generation

Descrizione del lavoro

Hired is partnering with a global AI leader to recruit a Senior Python Engineer for remote work. The role focuses on designing and optimizing Python-based pipelines to train and evaluate large language models, with emphasis on data generation, benchmarking, and model alignment.

You will collaborate with researchers to implement supervised fine-tuning and RLHF, contributing to high-quality datasets and reward models for continued model improvement.