About the job
AI research at WRITER isn't just about publishing papers — it's about building the scientific foundation that powers some of the most ambitious enterprise AI deployments in the world. As a staff AI research scientist, you'll be at the center of that work. You'll drive a high-impact research agenda focused on large language models, agentic reasoning, and the system-level capabilities that make AI genuinely useful at enterprise scale. This is a rare opportunity to do research that matters twice over — advancing the field and shipping directly into products used by hundreds of thousands of people every day.
Responsibilities
- Lead an independent, high-impact research agenda on large language models and agentic systems, owning projects from early hypothesis through model training, evaluation, and production deployment
- Design and execute large-scale post-training experiments using supervised fine-tuning, reinforcement learning from human feedback (RLHF), RLAIF, DPO, and emerging alignment techniques — with a focus on improving multi-step reasoning, planning, and tool use in enterprise agentic workflows
- Build novel evaluation benchmarks and methodologies that push beyond existing limitations, establishing rigorous measures for how well models perform on complex, real-world enterprise tasks
- Develop scalable data synthesis and curation pipelines that generate the high-quality training signal driving model improvement — including LLM-as-judge frameworks, synthetic data generation, and adversarial dataset construction
- Shape WRITER's model architecture and training roadmap by translating your research insights into concrete improvements to our enterprise-grade LLMs, working hand-in-hand with research engineering and product teams
- Publish and present original research at top-tier venues — NeurIPS, ICLR, ICML, ACL, and others — representing WRITER at the frontier of the field and contributing to the broader scientific community
- Mentor and uplevel fellow researchers and engineers on the team, helping set a high bar for scientific rigor, experimental design, and research culture
Qualifications
Minimum
- 7+ years of hands-on ML research experience, with deep expertise in large language model pre-training and post-training — you've trained models at scale, debugged distributed jobs, and shipped improvements that made a measurable difference
- Expert-level knowledge of post-training methods including SFT, RLHF, RLAIF, DPO, GRPO, and related alignment and reasoning techniques, with a track record of applying them to real, production-grade systems
- Strong command of Python and PyTorch (or JAX), with the engineering depth to build and scale training pipelines, evaluation infrastructure, and data synthesis workflows yourself — not just direct others to do it
- A meaningful publication record at competitive ML/AI venues (NeurIPS, ICLR, ICML, ACL, EMNLP, or equivalent), evidencing your ability to originate ideas and execute on a multi-month research agenda independently
- Hands-on experience designing or evaluating agentic systems — models that plan, reason through multi-step tasks, use tools, and recover gracefully from errors — with a nuanced understanding of where they break and how to fix them
- A Ph.D. in Computer Science, Machine Learning, NLP, or a related field — or equivalent demonstrated research experience with a strong portfolio of independent, published work
Preferred
No preferred qualifications listed.