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ServiceNow's Core AI Model Training team is building the next generation of enterprise language models that power AI experiences across the platform, serving 9,000+ enterprise customers worldwide. You will lead research and engineering across the full lifecycle of large language model development for enterprise use cases: data curation, pretraining, fine-tuning, and evaluation.
In this role, you will:
- Apply your AI/ML expertise and creativity to solve real-world challenges and datasets in practical, scalable ways
- Research, propose, implement, train, and evaluate models and techniques end-to-end
- Build and maintain training pipelines and evaluation harnesses that ensure reproducible results and measurable progress
- Collaborate daily with research scientists, engineers, and product managers to ship high-quality, high-impact work
- Own your work from design through implementation, testing, and delivery, partnering with product owners to translate requirements into results
- Continuously improve custom enterprise models by incorporating novel techniques and optimizing training and inference efficiency
You will help establish a differentiated advantage for AI applications across the platform while working with early-adopter customers to build an amazing range of solutions.
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 large-scale data curation, 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
- 6+ years of relevant experience with a Bachelor's degree, 4+ years with a 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, and KV-cache tradeoffs
- Publications at top-tier venues (ICLR, NeurIPS, ICML, ACL, EMNLP, AAAI)
- Fluency with AI productivity tools (Claude Code, Codex) and track record of integrating AI into engineering and research workflows
