Senior Machine Learning Engineer

Responsibilities

• Design, prototype, research and build AI systems for Vectara
• Train, evaluate and deploy ML models in the domains of Natural Language Processing, Information Retrieval, AI Agents, Large Language Models (LLMs) and Multimodal Large Language Model (MMLLMs)
• Improve the quality of Vectara’s AI Agents and RAG-as-a-service platform, working on features like agentic behavior, hallucination reduction/correction, and agent orchestration
• Publish technical blogs, papers, and patents

Requirements

• BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field
• 7+ or 8+ years of professional work experience applying machine learning to real-world problems
• Strong domain knowledge in RAG, LLM, information retrieval, or Multimodal LLMs
• Excellent programming skills in Python
• Proficiency in pandas, transformers, and torch
• Familiarity with deep learning concepts such as Transformers, RAG, and mixture of experts (MoE)
• Hands-on experience in training ML systems end-to-end from data curation to evaluation and deployment

Preferred Qualifications

• PhD in Computer Science/Engineering with 3+ years of industry experience
• Publications in prestigious venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, ICLR
• Experience as an ML engineer in an early-stage, high growth environment
• Expertise in embedding models, rerankers, multimodal retrieval, vector databases, BM25, or NLG Evaluation

Benefits

• 100% paid Medical, Dental, and Vision starting on day one
• Option of Health Savings Account (HSA) or Flexible Savings Account (FSA)
• Generous paid time off (PTO), paid sick time, holidays, and company rest days
• Professional development and training opportunities
• Company virtual happy hours and team building activities

About the Company

Vectara provides a scalable platform to deploy Enterprise AI Agents and AI Assistants with high accuracy, security, and explainability. Their enterprise RAG Platform leverages advanced models for retrieval, embedding, and reranking to ensure production-ready AI.