Energy-Storage
Excellent communication and collaboration skills, with the ability to effectively convey complex technical concepts to diverse stakeholders.
To learn more about SES, please visit: www.ses.ai
Lead the design and implementation of advanced NLP techniques and methodologies to extract chemical information including SMILES notations, properties, and interleaved text for multimodal language model training and chemical property predictions.
Proficiency with Git and Linux based systems and proficiency in programming languages such as Python, R, or Java, along with expertise in relevant libraries and frameworks (e.g., PyTorch, NLTK, TensorFlow).
Experience with cloud computing platforms and services (e.g., AWS, Azure, Google Cloud) for scalable data processing and storage.
Position Scope
At SES AI, we are at the forefront of revolutionizing lithium-metal battery creation with our groundbreaking approach that integrates cutting-edge machine learning techniques into our research and development processes. Our mission is to lead the next wave of scientific discovery in material science, powered by advanced AI technologies with a dedication to AI for Science.
Experience with AI agent’s studies, using knowledge-based Retrieval-Augmented Generation (RAG) to facilitate the accuracy of language generation.
Responsibilities
Knowledge of data visualization techniques and tools for exploring and presenting scientific insights.
Extensive hands-on experience in natural language processing, with a strong emphasis on designing and implementing language-based data labeling pipelines.
Advanced degree (master’s or PhD preferred) in computer science, data science, or a related field.
About the job
Senior Data Scientist, Natural Language Processing and Data Annotation Expert
Exceptional problem-solving skills with meticulous attention to detail, coupled with a passion for advancing scientific discovery through data science.
Preferred Qualifications
Stay abreast of the latest advancements in NLP and data annotation techniques, integrating emerging methodologies to enhance our data labeling capabilities.
Lead the design and implementation of advanced NLP techniques and methodologies to extract intricate scientific concepts and reasoning from vast textual sources.
Collaborate closely with cross-functional teams to identify key research areas and define labeling strategies to capture nuanced scientific insights effectively.
Conduct thorough analyses to evaluate the effectiveness of labeling pipelines and make continuous improvements to optimize performance.
Location: United States (Remote)
Proven track record of leveraging NLP techniques to extract complex scientific concepts and reasoning from textual sources.
Spearhead the development of innovative approaches for data annotation, incorporating state-of-the-art NLP algorithms to enhance accuracy and efficiency.
We are seeking a seasoned Senior Data Scientist specializing in Natural Language Processing (NLP) and Data Annotation to spearhead our innovative projects. The ideal candidate will possess exceptional expertise in NLP,. utilizing state-of-the-art multimodal language models to conduct the retrieval and extraction of intricate chemical information within the realms of material and battery science. Moreover, we are looking for an individual with a profound understanding of designing language-based data labeling pipelines to extract scientific chains of thought for advanced reasoning and discovery. This pivotal role will involve leading the creation of labeled datasets crucial for training our cutting-edge language models and AI agents.
This role will be remote.
www.ses.ai
Qualifications
Develop and refine language-based data labeling pipelines tailored for scientific discovery, ensuring high-quality annotated datasets for training large language models and AI agents.
Familiarity with deep learning models and architectures for NLP tasks, such as transformer-based models (e.g., BERT, GPT).
About us
Provide expert guidance on data annotation best practices, ensuring consistency and quality across labeled datasets.
Energy-Storage
More Information
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