CompanyWe are a technology product startup that is transforming the job market and career personalization space. Our advanced data mining techniques, computing optimizations, and state-of-the-art Natural Language Processing (NLP) techniques, including transformer and LLM-based architectures, enable large-scale processing of job market data*. We help job seekers find jobs that are the best fit for their skills and experience, and identify skill and credential gaps for their dream jobs.
*Currently our DB is bigger than +100x of all Wikipedia corpus
Job DescriptionNote: This is an unpaid internship position
As an NLP Engineering Intern - LLMs, Graph and RAG, you will work closely with our experienced NLP team on projects focused on graph-based and Retrieval-Augmented Generation (RAG) systems. You will design and implement systems that integrate NLP models with Neo4j and retrieval mechanisms, preprocess and clean large text datasets, and evaluate the performance of these hybrid systems. This role requires a solid foundation in NLP, machine learning, and a passion for advanced techniques.
Responsibilities
- Develop and implement NLP systems using graph-based methods (Neo4j) and RAG techniques.
- Preprocess and clean large-scale text datasets.
- Evaluate and fine-tune the performance of integrated NLP models.
- Collaborate with cross-functional teams to integrate solutions into products.
- Stay updated with advancements in graph-based NLP and RAG systems.
- Document and communicate work effectively to stakeholders.
- Participate in team meetings, discussions, and code reviews.
Qualifications- Currently pursuing a Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- Solid understanding of NLP techniques and language modeling.
- Experience with graph-based NLP methods and RAG systems.
- Familiarity with Neo4j, NLP libraries (e.g., NLTK, spaCy, Transformers), and Python libraries (e.g., NumPy, Pandas, TensorFlow/PyTorch).
- Strong problem-solving and analytical skills.
- Effective communication and teamwork abilities.
- Expertise with Linux operating systems.
Hiring Process- Take home project (3 days to complete)
- Interview with NLP Engineer - project & technical interview (60 mins)
- Interview with Chief Product Officer (30 mins)
Note: Only shortlisted candidates will be contacted for further steps in the selection process.