Senior Machine Learning Engineer

Vectara Vectara · Data AI · Remote · Platform Engineering and ML

Senior Machine Learning Engineer at Vectara, focusing on building and improving Enterprise AI Agents and RAG platforms. The role involves training, evaluating, and deploying ML models in NLP, Information Retrieval, AI Agents, and Multimodal LLMs, with an emphasis on agentic behavior, hallucination reduction, and orchestration. The company provides a scalable platform for AI Agents and Assistants with a focus on accuracy, security, and explainability.

What you'd actually do

  1. Design, prototype, research and build AI systems for Vectara.
  2. 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).
  3. 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.
  4. Publish technical blogs, papers, and patents.

Skills

Required

  • BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field.
  • 8+/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.

Nice to have

  • PhD in Computer Science/Engineering with 3+ years of industry experience.
  • Publications in prestigious venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, ICLR as a key author.
  • Experience as an ML engineer in an early-stage, high growth environment.

What the JD emphasized

  • Accuracy, Security, and Explainability
  • Accuracy, Security, and Explainability
  • Accuracy, Security, and Explainability
  • production-ready

Other signals

  • Enterprise AI Agents
  • RAG Platform
  • Hallucination Mitigation
  • LLM trained for quality
  • production-ready AI