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 and experience writing production-grade code
Proficiency with data/ML libraries (pandas, transformers, PyTorch) and hands-on experience training ML systems end-to-end (data curation to deployment)
Requirements:
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 multilinguality, self-supervised learning, agentic behavior, and hallucination reduction
Publish technical blogs, research papers, and patents
Job description
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.
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.
