ML-engineer/ DS - Fake News Detection

Key Responsibilities:

• Develop and deploy machine learning models and AI solutions for fake news detection, focusing on natural language processing (NLP) and data-driven insights.
• Research misinformation trends, detection methodologies, and mitigation strategies to advance the field.
• Build and fine-tune transformer-based architectures (e.g., BERT, GPT) for detecting fake news and misinformation at scale.
• Analyze large, complex datasets from social media, news platforms, and other sources to identify patterns and indicators of misinformation.
• Generate synthetic fake news content to create diverse datasets for model training, evaluation, and enhancement.
• Translate research findings into scalable systems for real-world applications.
• Collaborate with cross-functional teams, including data engineers, UX designers, and policy experts, to create integrated solutions.
• Publish findings in top-tier journals and conferences to contribute to the academic and practitioner community.

Requirements:

• Master’s, or Ph.D. in Computer Science, Machine Learning, Data Science, or related fields.
• 5+ years of professional experience in machine learning, with a proven track record in building and deploying ML systems.
• At least 1 year of hands-on experience in fake news detection, misinformation research, or a closely related domain.
• Strong programming skills in Python, with experience using libraries such as TensorFlow, PyTorch, or Hugging Face.
• Expertise in transformer-based models, sentiment analysis, and NLP.
• Experience working with fake news datasets or similar resources.
• Strong analytical skills and experience in applying research to solve real-world problems.
• Excellent communication and teamwork abilities, with a collaborative approach to problem-solving.