Language AI Researcher - Generative AI Applications - Intern

About wover.ai

Our contextually aware AI empowers employees to be more productive and focused on what really drives your business forward. Applying the latest in generative AI models, wover.ai supercharges your critical data, processes, and decision-making frameworks into seamless automated interactions, leaving your employees to be hyper focused on what matters and less on what can be automated.

We are on the lookout for AI superstars to contribute to the development of wover.ai’s innovative AI solutions that redefine how we work and get $#!+ done.

Interested? Come work with us.

About your role

We seek Pioneer’s, so if you are the take a walk in the woods type or have a healthy appetite for operating at the edge of possible and happen to be an AI Researcher interested in Domain Specific Models (DSM’s) and injecting contextuality into AI models, we want to hear from you.

We are seeking a highly capable and curious Applied Research Scientist (ARS) to join our Research team who are laser focused on building the context-aware, domain smarts AI platform that is empowering employees to reach their highest aspirations.

As a wover.ai Applied Research Scientist, you will be responsible for developing and implementing cutting-edge solutions that leverage the latest in generative AI, ML, and that inject contextuality into NLP technologies.

You will collaborate with a multidisciplinary team of researchers, engineers, and data scientists to explore and implement novel techniques that enable NLP models to comprehend and generate contextually relevant responses. This role requires a strong background in NLP, AI, and ML, combined with a passion for pushing the boundaries of language understanding and generation.

Working closely with cross-functional teams to understand business requirements, available data, and translate them into experiments, build the architecture and train the showcase models. You will work closely with Full Stack Engineers to expose the models as APIs and usable software applications. This is a highly technical Applied Research role that requires expertise in Large Language Models (LLMs), and all data pipeline tasks.

You will get to

〉 Conduct research and experimentation to develop and enhance context-aware LLMs
〉 Collaborate with researchers, data scientists, and engineers to define and implement research projects and initiatives
〉 Investigate and propose novel approaches for capturing and utilizing contextual information in language models
〉 Develop and train deep learning models for language understanding, generation, and context extraction
〉 Design and implement data preprocessing pipelines to curate and annotate contextual datasets
〉 Evaluate and benchmark model performance using appropriate metrics and design experiments to assess context-awareness effectiveness
〉 Stay up to date with the latest research advancements in NLP, ML, and LLMs
〉 Work closely with Software Engineers (SWEs) to integrate research findings into production systems
〉 Collaborate with cross-functional teams to understand industry-specific requirements and develop context-aware LLMs to inform DSM’s
〉 Document research findings, methodologies, and codebase to share knowledge and contribute to technical documentation
〉 Optimize and fine-tune AI models for improved accuracy, performance, and scalability

You will have

〉 Master's or Ph.D. degree in Computer Science, Electrical Engineering, or a related field with a focus on NLP, AI, and ML
〉 Strong theoretical and practical knowledge of NLP techniques, including language modeling, sentiment analysis, and named entity recognition
〉 Proven experience in developing and training large-scale language models (e.g., GPT, BERT, Transformer).
〉 Proficiency in deep learning frameworks such as TensorFlow or PyTorch
〉 Strong programming skills in Python and familiarity with Software Engineering best practices
〉 Experience with data preprocessing, data cleaning, and dataset curation for training machine learning models
〉 Solid understanding of machine learning algorithms and statistical methods
〉 Demonstrated track record of publications or contributions to the field of NLP, AI, and ML
〉 Experience with advanced techniques such as transfer learning, self-supervised learning, or reinforcement learning
〉 Strong problem-solving skills and the ability to analyze complex systems and architectures with the ability to think critically and creatively
〉 Demonstrated ability to learn new technologies and adapt quickly to evolving project requirements
〉 Strong communication skills, both written and verbal, to effectively convey complex concepts and research findings
〉 Ability to work independently as well as collaboratively in a fast-paced research and development environment

Bonus Points

〉 Demonstrable experience in Content Moderation, Text Classification, Semantic Search, User Intent Recognition, Text Generation, Entity Extraction, Text Summarization, Chat
〉 Experience in developing context-aware systems, recommendation engines, or personalized user experiences using NLP, AI, and ML techniques
〉 Demonstrated track record of publications or contributions to the field of NLP or machine learning
〉 Knowledge of domain-specific contextual understanding, such as finance, healthcare, or legal