Machine Learning Engineer, GAI Search Platform - Moveworks

ServiceNow•Mountain View, CA

115d•$139,700 - $216,500•Onsite

About The Position

The Machine Learning Engineer will join Moveworks’ GenAI Search Platform team to improve search-based question-answering systems using traditional machine learning and generative AI. The team builds conversational search systems that give enterprise users instant answers by combining information retrieval with natural language understanding and large language models. Its products retrieve, match, rank, and summarize text grounded in heterogeneous knowledge sources. The role works closely with ranking, product, design, infrastructure, and data science teams to advance agentic search capabilities, performance, and enterprise readiness. The engineer will help product teams build applications and features, prototype at scale, systematically evaluate and improve ranking, and enforce quality, security, privacy, and scalability. The position also develops evaluation metrics and methodologies for search models, using LLMs to address enterprise-specific challenges.

Requirements

At least 3 years of engineering experience.
First-hand experience relevant to search platforms, such as traditional information retrieval, search ranking and relevance, recommendation systems, or question-answering systems.
Background in text processing, information retrieval, natural language understanding, or machine learning.
Ability and interest to partner with product and ranking teams to help their solutions move faster and scale, with a focus on customer impact and success.

Nice To Haves

Experience with, or desire to learn, large language models, retrieval-augmented generation (RAG), and agents.
Experience in Python; contributions to mature Python libraries or platform code, including open-source repositories, are a plus.
Experience in Golang (not required).

Responsibilities

Drive development of the search platform and new LLM-enabled generative AI features.
Enable product teams to build applications and features quickly and ship prototypes to customers at scale.
Evaluate and iterate on ranking improvements systematically.
Help enforce quality, security, privacy, and scalability guarantees.
Develop systematic evaluation metrics and methodologies for search models, leveraging LLMs to address enterprise security and privacy challenges.
Partner with product, user experience, customer success, and engineering teams to identify, define, and build solutions.