The opportunity
WRITER is the AI platform built for the world’s most ambitious marketing and revenue teams, bringing the frontier of AI to the people who drive enterprise growth. With WRITER, autonomous agents work across the buyer journey, continuously executing on market signals and enterprise intelligence to grow revenue faster.
What you'll do
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
What they're looking for
- Mentor and uplevel fellow researchers and engineers on the team, helping set: a high bar for scientific rigor, experimental design, and research culture
- + 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
- 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
- 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