The Company is looking to solve problems in Natural Language Processing, Information Retrieval, AI Agents, Large Language Models (LLMs), and Multimodal Large Models (MLMs) by designing, prototyping, researching, and building AI systems.
Requirements
- Knowledge of common challenges in training machine learning models and best-practice solutions.
- Familiarity with deep learning concepts such as Transformers, Retrieval-Augmented Generation (RAG), and Mixture of Experts (MoE).
- Proficiency in data/ML libraries such as pandas, transformers, and torch.
- Hands-on experience training ML systems end-to-end, from data curation to evaluation and deployment.
- Expertise includes embedding models, rerankers, multimodal retrieval, question answering, reasoning, vector databases, and BM25.
- Skilled in planning and reasoning in LLMs, multilinguality in LLMs, and NLG evaluation, including hallucination detection.
Responsibilities
- Design, prototype, research, and build AI systems for the Company.
- Train, evaluate, and deploy ML models in Natural Language Processing, Information Retrieval, AI Agents, Large Language Models (LLMs), and Multimodal Large Models (MLMs).
- Improve the quality of the Company's RAG-as-a-service platform, including areas such as multilinguality, self-supervised learning, agentic behavior, and hallucination reduction.
- Publish technical blogs, research papers, and patents.
Other
- BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field.
- 4+ years of experience after BS/MS.
- Ability to collaborate effectively with cross-functional teams.
- PhD in Computer Science/Engineering with 1+ years of industry experience (preferred)
- Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author.
