Job description

The Role We are looking for an experienced software engineer with machine learning expertise to join us in expanding Moveworks agentic AI capabilities, enabling increasingly magical user experiences and improving Moveworks agentic and conversational AI capabilities platform-wide. As a member of the Agentic AI Harness & Quality team, you will have all the tools of modern agentic AI engineering at your disposal, from best-in-class LLMs, multimodal foundation models, and hybrid vector databases to all the infrastructure needed to tune, evaluate, and serve both models and agents in production. We are an eval-driven, data-centric team, and you will have the assistance of a world-class annotation team to build error-free, inclusive, and privacy-preserving datasets for tuning and evaluation. You will also go beyond model training to achieve state-of-the-art AI performance in production, in every meaning of the word “performance”: not just accuracy and quality of outputs, but also latency, reliability, and capability of the end-to-end user-facing system as a whole. Successful machine learning engineers on the team are motivated to design and maintain high-performing compound AI systems, with model training as one tool among many in the toolbox rather than the central responsibility of the job. Our team indexes on increasing our ability to move fast, solving challenging product and engineering challenges, and pushing the envelope of agent reliability and value provided to customers. Your work will impact our team’s core objective to build the highest-performing enterprise assistant platform the world has ever seen, in close collaboration with other functions within Moveworks and ServiceNow as a whole. What you get to do in this role: • Apply software engineering, machine learning, and compound AI system engineering to create lasting value for all our customers • Drive quality in agent behavior, both in terms of increasing utility for end users and decreasing misbehavior in the customer business environment • Take on exciting and difficult challenges in AI harness engineering, such as agent cognitive architecture, eval benchmark creation, context engineering, generative UI, dynamic agent orchestration, multimodal I/O, multilinguality, conversational memory management, reasoning strategies, abstractive summarization, grounding and verifiability for generated text, deployment safety, and self-learning • Read, discuss, and build on the latest ML/LLM research and open-source repositories and models • Research and develop innovative, scalable and dynamic solutions to hard problems • Use your knowledge of machine learning fundamentals and LLMs to make improvements to the agentic harness and its AI components, evaluate them with small scale experiments and productionize your solutions at scale • Partner closely with web and chat platform engineering teams to turn harness investments into delightful user experiences that wow our customers To be successful in this role you have: • Drive to ship product improvements with production-quality, fully unit-tested code and rigorously-evaluated updates to models, prompts, or other tunable system components • Ability to solve problems end-to-end with machine learning • Solid grasp of model evaluation fundamentals, especially for agent trajectories, text generation, text classification, and non-uniform sampling regimes • Attention to detail and high standard of data quality for training and especially evaluation datasets • Readiness to hit the ground running in a Mac development environment, programming in Python and/or Golang • Familiarity with deep learning architectures and algorithms and leading large language models • Desire to work at a startup pace in a medium-sized company with a high degree of ownership • Drive to ship product improvements with production-grade code • Strong appetite for continuous incremental wins and completing challenging projects fast • High level of curiosity about engineering outside of immediate discipline and ongoing desire to learn and stay at the cutting edge of NLU & AI Work Personas We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here . To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. Accommodations We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance. Export Control Regulations For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities. From Fortune. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.

Requirements

Must-haves

What you get to do in this role:
Apply software engineering, machine learning, and compound AI system engineering to create lasting value for all our customers
Drive quality in agent behavior, both in terms of increasing utility for end users and decreasing misbehavior in the customer business environment
Read, discuss, and build on the latest ML/LLM research and open-source repositories and models
Research and develop innovative, scalable and dynamic solutions to hard problems
Use your knowledge of machine learning fundamentals and LLMs to make improvements to the agentic harness and its AI components, evaluate them with small scale experiments and productionize your solutions at scale
Partner closely with web and chat platform engineering teams to turn harness investments into delightful user experiences that wow our customers
To be successful in this role you have:
Drive to ship product improvements with production-quality, fully unit-tested code and rigorously-evaluated updates to models, prompts, or other tunable system components
Ability to solve problems end-to-end with machine learning
Solid grasp of model evaluation fundamentals, especially for agent trajectories, text generation, text classification, and non-uniform sampling regimes
Attention to detail and high standard of data quality for training and especially evaluation datasets
Readiness to hit the ground running in a Mac development environment, programming in Python and/or Golang
Familiarity with deep learning architectures and algorithms and leading large language models
Desire to work at a startup pace in a medium-sized company with a high degree of ownership
Drive to ship product improvements with production-grade code
Strong appetite for continuous incremental wins and completing challenging projects fast
High level of curiosity about engineering outside of immediate discipline and ongoing desire to learn and stay at the cutting edge of NLU & AI
Work Personas

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaigomlllmpythongolang

Platform: smartrecruiters
Effort: medium