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Role Description

• 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 AI Agents and RAG-as-a-service platform, including features such as agentic behavior, hallucination reduction/correction, and agent orchestration.
• Publish technical blogs, research papers, and patents.

Qualifications

• BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field.
• 7+ years of professional work experience after BS/MS applying machine learning to real-world problems, and crafting scalable and effective ML/AI solutions.
• Strong domain knowledge in at least one of the following: RAG, LLM, information retrieval, Multimodal LLMs.
• Excellent programming skills in Python.
• Proficiency in data/ML libraries such as pandas, transformers, and torch.
• Familiarity with the technical details of deep learning concepts, such as Transformers, Retrieval-Augmented Generation (RAG), mixture of experts (MoE).
• Hands-on experience in training ML systems end-to-end from data curation to evaluation and deployment.
• PhD in Computer Science/Engineering with 1+ years of industry experience.
• Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author.
• Experience as an ML engineer in an early-stage, high-growth environment.
• 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.

Requirements

• BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field.
• 7+ years of professional work experience after BS/MS applying machine learning to real-world problems, and crafting scalable and effective ML/AI solutions.
• Strong domain knowledge in at least one of the following: RAG, LLM, information retrieval, Multimodal LLMs.
• Excellent programming skills in Python.
• Proficiency in data/ML libraries such as pandas, transformers, and torch.
• Familiarity with the technical details of deep learning concepts, such as Transformers, Retrieval-Augmented Generation (RAG), mixture of experts (MoE).
• Hands-on experience in training ML systems end-to-end from data curation to evaluation and deployment.
• PhD in Computer Science/Engineering with 1+ years of industry experience.
• Publications in top-tier venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR as a key author.
• Experience as an ML engineer in an early-stage, high-growth environment.
• 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.