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Salary: USD 185,500 - 315,400 / annual
ServiceNow's Core AI Model Training team is seeking a Senior Research Engineering Scientist to lead research and engineering across the full lifecycle of large language model development for enterprise use cases. You will work on data curation, pretraining, fine-tuning, and evaluation to continuously improve custom enterprise models and establish differentiated advantages for AI applications across the platform.
In this role, you will confront real-world challenges and datasets, applying AI and ML expertise to solve problems in practical and scalable ways. You'll research, propose, implement, train, and evaluate models and techniques end-to-end. You'll build and maintain training pipelines and evaluation harnesses that ensure reproducibility and measurable progress. Daily collaboration with research scientists, engineers, and product managers will be essential to shipping high-quality, high-impact work. You'll own your work from design through implementation, testing, and delivery, partnering with product owners to translate requirements into results.
These models power a wide range of use cases across multiple business units, enabling teams to solve complex problems and deliver tailored solutions to more than 9,000 enterprise customers worldwide. ServiceNow is in the early stages with adopter customers and is building the next generation of enterprise language models that bring AI experiences into customers' day-to-day work.
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 (FSDP, DeepSpeed, Megatron) and parallelism strategies (tensor, pipeline, expert, 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 (decoder-only, autoregressive, encoder-decoder, mixture of experts)
- Expert-level Python with strong OOP and design pattern fundamentals
- Ability to read current research and rapidly prototype and experiment with new ideas
- 3+ years relevant experience with Bachelor's degree; 3+ years with Master's degree; PhD or equivalent experience
PREFERRED QUALIFICATIONS:
- Grounding in evaluation and benchmarking including building evaluation harnesses, contamination checks, and custom enterprise benchmarks
- Pretraining experience or end-to-end perspective across pretraining and post-training
- Familiarity with inference and serving technologies (vLLM, SGLang, quantization, KV cache tradeoffs)
- Publications at top-tier venues (ICLR, NeurIPS, ICML, ACL, EMNLP, AAAI)
- Fluency with AI productivity tools (Claude, Code, Codex) with track record of integrating AI into engineering and research workflows
