Senior Research Engineering Scientist

ServiceNow•Santa Clara, CA

About The Position

The Core AI Model Training team leads research and engineering across the full lifecycle of large language model development for enterprise use cases, including data curation, pretraining, fine-tuning, and evaluation. The team aims to continuously improve custom enterprise models by incorporating novel techniques and optimizing training and inference efficiency. These models support use cases across multiple business units and help deliver tailored solutions to 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 ones to solve problems practically and at scale. You will research, propose, implement, train, and evaluate models and techniques end to end; build and maintain reproducible training pipelines and evaluation harnesses; and collaborate with research scientists, engineers, and product managers. You will own work from design through implementation, testing, and delivery, working with product owners to turn 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 and large-scale training, including FSDP, DeepSpeed, and Megatron.
Experience with parallelism strategies such as tensor, pipeline, expert, and data parallelism.
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.

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, including understanding 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, with a track record of thoughtfully integrating AI into engineering and research workflows.

Responsibilities

Apply AI and machine-learning expertise and creativity to real-world challenges and datasets, using or developing methods that solve problems practically and at scale.
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.
Collaborate with research scientists, engineers, and product managers to deliver high-quality, high-impact work.
Own work from design through implementation, testing, and delivery, partnering with product owners to translate requirements into results.