Senior Staff Machine Learning Engineer

Job Description
Team
The Foundation Models Lab at ServiceNow Research builds the foundation for ServiceNow’s bespoke generative AI solutions, specifically tailored to meet the unique demands of our enterprise environment. Renowned for co-leading the BigCode project and releasing influential models like StarCoder and StarCoder 2, along with datasets such as the Stack and the Stack v2, our lab is now dedicated to strategic pretraining of foundation models for enterprise applications. Our mission extends beyond mere training; we aim to holistically design large language models considering their entire lifecycle. This includes curating training data to meet specific performance expectations, maximizing training efficiency, and, crucially, optimizing models for fast and cost-effective inference. By doing so, we ensure that our AI models are robust, efficient, and ready for further refinement and integration into ServiceNow’s broader AI ecosystem.
Role
We are seeking a Senior Staff Research Developer with deep expertise in AI research, particularly focused on planning and executing experiments that push the boundaries of AI training and model efficiency. In this role, you will:
Lead experimental research projects that optimize our large language models, focusing on reducing inference latency, integrating mixture-of-experts and related techniques, and applying scaling laws to maximize training efficiency.
Innovate on data curation and utilization strategies to enhance model training, developing sophisticated filtering and selection techniques that improve data quality and directly boost downstream performance.
Publish findings and contribute to the scientific community through impactful papers and presentations, thereby establishing ServiceNow as a leader in AI research.
Work autonomously, demonstrating resoluteness and resourcefulness in tackling complex research challenges, and continuously exploring new methodologies to refine our AI models.
Help the team deeply understand the contexts and purposes in which the models will be used to ensure their adequacy and maximize their impact.
Mentor other team members and facilitate effective collaboration among coworkers & cross-functionally.