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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 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.

Qualifications

• BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field.
• 4+ years of experience after BS/MS.
• Strong software engineering fundamentals; role involves research as well as writing production-grade code.
• 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.
• 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.
• Experience working 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.
• 4+ years of experience after BS/MS.
• Strong software engineering fundamentals; role involves research as well as writing production-grade code.
• 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.
• 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.
• Experience working 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.