Senior Research Engineering Scientist

ServiceNow•Santa Clara, CA

2d•$185,500 - $315,400

About The Position

The Core AI Model Training team leads research and engineering throughout the lifecycle of large language model development for enterprise use cases, including data curation, pretraining, fine-tuning, and evaluation. The team aims to improve custom enterprise models through novel techniques and greater training and inference efficiency, supporting AI applications across the ServiceNow platform. The models serve multiple business units and more than 9,000 enterprise customers worldwide. In this role, you will apply AI and machine learning expertise to real-world challenges and datasets, using existing methods and developing new approaches that are practical and scalable. You will conduct model research and development end to end, build reproducible training pipelines and evaluation tools, and collaborate with research, engineering, and product colleagues to deliver high-impact work. You will own projects from design through implementation, testing, and delivery, translating product requirements into results.

Requirements

Expertise in LLM post-training, including supervised fine-tuning and distillation.
Expertise in reinforcement learning for LLMs, including PPO, GRPO, DPO, and reward modeling.
Hands-on experience with training frameworks and distributed, large-scale training, including FSDP, DeepSpeed, and Megatron, as well as tensor, pipeline, expert, and data parallelism strategies.
Experience with synthetic data generation for training and evaluation.
Experience with data curation at scale, including deduplication, filtering, and data mixture design.
Familiarity with transformer architectures, including decoder-only or autoregressive, encoder-decoder, and mixture-of-experts models.
Expert-level Python skills and strong object-oriented programming and design-pattern fundamentals.
Ability to read current research and rapidly prototype and experiment with new ideas.
At least 3 years of relevant experience with a bachelor's degree, or at least 3 years with a master's degree; a PhD or equivalent experience is also accepted.

Nice To Haves

Grounding in evaluation and benchmarking, including building evaluation harnesses, contamination checks, and custom enterprise benchmarks.
Pretraining experience or an end-to-end perspective across pretraining and post-training, with an understanding of how choices in one stage affect the other.
Familiarity with inference and serving technologies such as vLLM or SGLang, quantization, and KV-cache tradeoffs.
Publications at top-tier venues such as ICLR, NeurIPS, ICML, ACL, EMNLP, or AAAI.
Fluency with AI productivity tools such as Claude Code and Codex, and a track record of thoughtfully integrating AI into engineering and research workflows.

Responsibilities

Research, propose, implement, train, and evaluate models and techniques end to end.
Build and maintain training pipelines and evaluation harnesses that make results reproducible and progress measurable.
Apply AI and machine learning expertise and creativity to real-world challenges and datasets, using existing methods and developing new ones to solve problems practically and at scale.
Collaborate with research scientists, engineers, and product managers to ship high-quality, high-impact work.
Own work from design through implementation, testing, and delivery, partnering with product owners to translate requirements into results.