Develop and Optimize AI Models: Build, train, and fine-tune machine learning models that process large volumes of news data, summarize articles, and extract key insights.
Text Summarization & NLP: Design and implement NLP algorithms for both extractive and abstractive summarization, ensuring summaries are coherent, concise, and contextually relevant.
Context Analysis & Sentiment Extraction: Develop models that can understand and analyze the context and sentiment behind news articles, including tone, intent, and underlying themes.
tem Design: Integrate human feedback mechanisms to continuously improve AI model performance, addressing challenges like biases, accuracy, and alignment with user preferences.
Model Evaluation & Iteration: Continuously assess and improve model accuracy and reliability through A/B testing, user feedback, and data analysis.
Collaboration: Work with cross-functional teams including product managers and other engineers to ensure models align with business objectives and technical requirements.
Data Collection & Annotation: Assist in sourcing and curating high-quality training datasets, including news articles, to ensure the AI models are trained with diverse and accurate information.
Skills & Qualifications:
Machine Learning & AI Knowledge: Strong understanding of machine learning algorithms, with hands-on experience in applying them to natural language processing and text analysis tasks (e.g., classification, clustering, summarization).
Natural Language Processing (NLP): Expertise in key NLP techniques such as abstractive and extractive summarization, named entity recognition (NER), topic modeling, sentiment analysis, and semantic parsing
.Human-in-the-Loop Systems: Experience in designing systems where human input is integrated to refine and improve model outputs iteratively.
Programming Languages: Proficiency in Python and relevant machine learning libraries like TensorFlow, PyTorch, spaCy, Hugging Face Transformers, sci-kit-learn, or NLTK
.Deep Learning Frameworks: Familiarity with deep learning techniques and frameworks, particularly for NLP tasks (e.g., RNNs, LSTMs, Transformers)
Data Processing & Feature Engineering: Experience with data wrangling, text preprocessing, and feature extraction techniques for unstructured text data.
Contextual & Sentiment Analysis: Ability to design models that understand subtle nuances in language such as sarcasm, irony, and other complex sentiments within the news.
Evaluation Metrics & Model Fine-Tuning: Familiarity with performance metrics such as precision, recall, F1 score, and BLEU for evaluating NLP models.
Version Control & Collaboration Tools: Experience with version control systems (e.g., Git) and collaboration platforms (e.g., Jira, Confluence) for agile development.
Problem-Solving & Critical Thinking: Strong analytical skills with the ability to troubleshoot model issues, debug code, and suggest improvements based on testing and evaluation results.
Preferred Qualifications:
Master’s degree or PhD in Computer Science, Data Science, Machine Learning, or a related field. Experience with large-scale real-time data processing and deployment of machine learning models into production environments. Familiarity with cloud platforms like AWS, GCP, or Azure for deploying and scaling machine learning models. Knowledge of ethics in AI and bias mitigation strategies for NLP systems.
Key Language and Skills:
Programming: Proficiency in Python, experience with libraries such as TensorFlow, PyTorch, spaCy, and Hugging Face. NLP Expertise: In-depth knowledge of abstractive and extractive summarization, named entity recognition, sentiment analysis, and contextual understanding. Machine Learning Algorithms: Expertise in classification, clustering, and deep learning models tailored to NLP. Human-in-the-Loop (HITL): Experience integrating human feedback into the AI model development cycle. Model Evaluation: Strong understanding of performance metrics for NLP models, such as F1 score, BLEU, and other text-based evaluation metrics. Collaboration Tools: Familiarity with Git, Jira, and cloud-based platforms for version control and project management. This revised version focuses more on the Machine Learning Engineer role, emphasizing the technical expertise required, without including the Chief of Staff role. It provides a comprehensive look at the candidate's skills in NLP, HITL systems, and practical AI deployment.
Why Join Us?
Work at the cutting edge of AI technology and contribute to real-world applications in the media and news sectors. Be part of a fast-paced, collaborative team focused on solving complex, high-impact challenges. Opportunity for personal and professional growth as you work on challenging projects in the rapidly evolving field of AI.
What We Offer
Opportunity to grow within the company Competitive salary and performance-based bonus PTO and paid holidays Loan Laptop Allowance Opportunity to relocate to Canada Flexible Hours team-building events. A supportive, close-knit work environment
معرفی شرکت
Roomvu offers Automated VIDEO marketing, curated by A.I. which integrates trending topics with REALTOR brands to automatically produce, publish, and post unique videos on each realtors’ social media channels. it generates leads for over 130,000 Real Estate Agents.
Roomvu also offers eye catching VIDEO ADS for Facebook Ads for Real Estate, Where agents can Reach leads they otherwise would never have had the opportunity to meet.
Roomvu is backed by the National Association of Realtors’s Second Century Ventures and works with industry leaders in the real estate to facilitate its growth
Our Global team of 80, has grown substantially over the past 5 years and we are excited to welcome you aboard the interview process.
Read more here:
https://www.inman.com/2021/02/09/roomvu-adds-digital-ads-to-its-automated-video-marketing
You can also learn more in this short video:
https://www.youtube.com/watchv=LCAaPi2LddA&ab_channel=roomvu