- Prediction of type of work component (phase, task and activity) from time entries and narratives
- Identification and discovery of categories (topic modeling) from unlabeled text data collections
- Prediction of narrative templates (andfull generation of narratives) from timecard history of matters
- Document segmentation and categorization of terms in Outside Counsel Guidelines
- Enrichment of current data representations by generating new features and rich metadata
- Automatic collection and generation of ML/NLP resources for theProfessionalServices industry
- Prediction of system failures from cloud component activity logs
- Contribute to the research, design and construction of cutting-edgeML/NLPsystems, applying emerging AI technologies to specific problems in the Professional Services industry.
- Conduct project activities related to exploratory analysis and experimentation. Document, report and analyze results. Propose new ideas and related experimental work based on result analysis.
- Workcloselywithourteam of data scientists and ML engineers,attend technical meetings and contribute to the discussions.
- The experience of being part of a growing public enterprise software company
- Enrolled and working towards obtaining a BS, MS, orPh.Din Computer Science, ArtificialIntelligence, Machine Learning, Data Science, Statistics orarelated technical field.
- Knowledge in machine learningand natural language processing, as well as computer science fundamentals(data structures, algorithms, big-O notation, concurrency, etc.)
- Strong background in probability and statistics: statistical measures, distributions, hypothesis testing, regressions, etc.
- Proficient in using Python(numpy,scipy,pandas,scikit-learn, spacy,pytorch, etc.)to implement machine learning models and algorithms.
- Experience working with both relational SQL and NoSQL data stores.
- Familiarity withthemost commonly usedMachine Learning algorithms and techniques (KNN, SVM, CRF, MLP, CNN, RNN, LSTM, etc.).
- Familiarity with commonly used Natural Language Processing pipelines and techniques (POS tagging, Parsing, NER, NLU, TFIDF weighting, word embeddings, etc.)
- Strong written and verbal communication skills; ability to explain technical concepts to a non-technical audience.
- Public Cloud AWSand/orAzure experience
- Some low-level programming experience (C,C++,Java,Go,etc.)
- Experience with big data & emerging technologies (Spark, Elasticsearch, Kafka, etc.)
- Our Summer 2024 Internship Program begins June 3rd, 2024 and is projected to end between August 16th and 23rd, depending on your university schedule.
- Your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, and engaging events with other interns and company leadership.
- Our internship is hybrid and interns will be expected in the office at least 3 days a week to get the full experience of the Intapp culture and networking opportunities, however, we will evaluate as Summer 2024 approaches.
- Please visit Intapp Summer Internship to hear from our former interns about their internship experiences and check out Working at Intapp to learn more about what we value and why Intapp is a wonderful place to start your career
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Machine Learning and NLP Engineer Intern - Palo Alto, CA, United States - Intapp
Description
Machine Learning & Natural Language Processing (Data Science) Engineer Intern
Location: Palo Alto, CA The successful candidate will work with world-classdata scientists andMLengineers on expandingthe AI capabilities ofIntappproducts. If you have a passion for machine learning,experience withnatural language processingand a demonstrated history of delivering results, then we are very interested in talking to you. What you'll do: The main objective of theML/NLP Internship atIntappis to use different learning paradigms and architectures tobuildpredictive models for different type of problems, including text categorization, natural language understanding and generation, data clustering and time series analysis, among others.The work will be applied to one (or more, if permitted by time) of the following problems of interest: