Applied Scientist- II

Train and deploy Language Models adapted to specific industry needs. Create and adapt novel training and fine-tuning algorithms for language models with special focus on reinforcement learning reinforcement learning for long-horizon dynamic workflow. Research innovation and scholarly dissemination: conceive and execute research projects that advance training methodologies; write and submit peer‑reviewed papers or preprints; and present work at conferences. Drive end‑to‑end translation of research into product capabilities, leading projects from ideation and prototyping through production integration and measurable customer impact. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research). OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research). OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field. OR equivalent experience. 1+ years of experience training/fine tuning AI/ML models, preferably large language models. 1+ years of experience building Generative AI pipelines, e.g. with RAG (Retrieval augmented generation). 1+ years of experience with Python and/or PyTorch. Experience with multi-agent training in dynamic harness. Experience training or contributing to the development of very large-scale language models (e.g., 100B+ to trillion-parameter models), including distributed training, async RL, and long sequence handling.