Research, prototype and evaluate ways of extracting structure and meaning from scholarly content – entities, relations, topics and emerging research areas – and of representing it in knowledge graphs and ontologies that can power better discovery, search and recommendation across Taylor & Francis.
Locations (2)
Bengaluru, Karnataka, India
New Delhi, Delhi, India
Responsibilities
Develop NLP pipelines for tasks such as named-entity recognition, relation extraction, entity linking and disambiguation, topic modelling and classification over scholarly text, using transformer models and LLMs.
Build, populate and query knowledge graphs in a graph database or triple store (e.g., Neo4j, RDF/SPARQL stores) and evaluate their quality and coverage.
Support the design and expansion of ontologies, taxonomies and controlled vocabularies, working with Editorial and subject-matter experts.
Detect and track emerging topics and research areas using clustering, topic modelling and embedding-based techniques.
Evaluate embedding models, chunking strategies, rerankers and retrieval configurations for semantic search and RAG on scholarly content.
Requirements
Bachelor’s degree in computer science, data science, computational linguistics, mathematics or a related field
Seeking a highly skilled and experienced Senior Data Scientist with a minimum of 8 - 11 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture.
Responsibilities
Contribute to the design and implementation of state-of-the-art AI solutions.
Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.
Collaborate with stakeholders to identify business opportunities and define AI project goals.
Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
Utilize generative AI techniques, such as LLMs, to develop innovative solutions for enterprise industry use cases.
Requirements
Bachelor's degree in Computer Science, Engineering, or a related field
Master's degree in Computer Science, Engineering, or a related field
Postdoctoral researcher in clinical AI and digital health to join the Division of Biomedical Informatics & AI at MUSC.
Compensation
Pay Range 0.00 - 0.00 - 0.000
Responsibilities
Acquire, curate and preprocess EHR, clinical notes, wearable and mobile data, and design, train and evaluate machine learning, deep learning and language models.
Lead analyses and first-author manuscripts for peer-reviewed journals and conferences, and present findings at national meetings.
Contribute to NIH grant proposals, including preliminary data and aims development.
Work with collaborators to define clinically meaningful outcomes and validate models.
Help mentor graduate and undergraduate students in the lab.
Requirements
PhD in biomedical informatics, computer science/engineering, data science, biomedical engineering, biostatistics or a related field
Nice to have: Interest in pursuing a K award or other early-career funding
Lead research at the intersection of industry-scale data and academic rigor in Document Understanding, designing and implementing AI-native agentic systems to unlock insights from billions of historical and genealogical records.
Responsibilities
Innovate with State-of-the-Art AI by implementing cutting-edge AI solutions for key Document Understanding tasks such as OCR/HTR, transcription, Named Entity Recognition, Relation Extraction, Coreference Resolution, Summarization, and Knowledge Graphs
Architect Agentic Systems by designing and implementing multi-agent workflows using frameworks like LangChain, LangGraph, CrewAI, AutoGen, AgentCore, Strands, Google ADK, A2A
Analyze and Optimize Multi-Modal Models by evaluating performance of multi-modal AI & LLMs such as GPT, Gemini, Claude, Llama, and Qwen for zero-shot and few-shot scenarios
Principal Engineer for On-Device AI Inference & Systems, owning the inference and integration stack end-to-end to make state-of-the-art multi-modal models run fast, small, and reliably within a browser-native runtime for game experiences.
Compensation
Zone A: $250,300 - $325,400
Zone B: $222,600 - $289,400
Zone C: $197,400 - $256,600
Responsibilities
Own the end-to-end optimization pipeline: model export, graph transformation, operator fusion, memory-layout planning, and hardware-specific kernel tuning across NPU, mobile GPU, and desktop/laptop GPU.
Make authoritative decisions on quantization (INT4/INT8/FP16), weight sharing, structured/unstructured pruning, and knowledge distillation to hit hard latency, memory, and power budgets.
Design and own the integration between the ML runtime and the game engine: real-time scheduling, threading, memory pooling, zero-copy buffer sharing between the inference path and the render path, and frame-budget management alongside the renderer.
Data collection, data analysis, model development, defining quality metrics, quality assessment of models and regular presentations to stakeholders.
Creating production-ready Python packages for each component of data science pipelines (such as pre-processing and model inference) and their deployment together with software engineering team
Optimizing and customizing Retrieval Augmented Generation (RAG) pipelines to meet specific project requirements that involve content ingestion, machine translation, and contextualized information retrieval
Ingesting, preprocessing, and transforming large-scale multilingual data to ensure high-quality inputs for downstream models.
Building AI agentic models integrated with RAG pipelines.
Requirements
Master’s or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related field.
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