Senior NLP Research Engineer

Description

Dream is a pioneering AI cybersecurity company delivering revolutionary defense through artificial intelligence. Our proprietary AI platform creates a unified security system safeguarding assets against existing and emerging generative cyber threats. Dream's advanced AI automates discovery, calculates risks, performs real-time threat detection, and plans an automated response. With a core focus on the ""unknowns,"" our AI transforms data into clear threat narratives and actionable defense strategies. 

Dream's AI cybersecurity platform represents a paradigm shift in cyber defense, employing a novel, multi-layered approach across all organizational networks in real-time. At the core of our solution is Dream's proprietary Cyber Language Model, a groundbreaking innovation that provides real-time, contextualized intelligence for comprehensive, actionable insights into any cyber-related query or threat scenario. 

We are seeking a highly skilled and experienced Senior NLP Research Engineer to join our AI research team and drive the development of Dream's next-generation large language models (LLMs). The ideal candidate will have a strong background in NLP, deep learning, and applied research, with a passion for innovating and pushing the boundaries of generative AI technologies.

Responsibilities

• Conduct cutting-edge research in natural language processing and large language models.
• Design, train, and optimize large-scale neural network models for advanced applications.
• Transition research projects from ideation through deployment and scaling.
• Collaborate closely with cross-functional teams, including domain experts, product managers, and engineers, to deliver impactful AI solutions.
• Define and contribute to the AI and NLP product roadmap.

Requirements

• M.Sc. in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
• 5+ years of experience in applied AI research or machine learning engineering.
• Proven expertise in designing, implementing, and evaluating deep learning models in a production environment.
• Strong programming skills, particularly in Python and ML frameworks (e.g., TensorFlow, PyTorch).
• Solid understanding of NLP techniques (transformers, embeddings, tokenization).
• Exceptional problem-solving abilities and a collaborative mindset.

Preferred Qualifications

• Experience in scaling ML models for production.
• Knowledge of distributed computing and infrastructure for training large models.
• Interest in exploring novel architectures for LLMs.

Skills