Applied Sciences 2

Design and implement advanced LLM-based architectures and agentic systems for real-world product scenarios. Collaborate across teams to deliver robust, scalable models aligned with product objectives and user value. Apply and adapt research ideas to solve practical challenges in reasoning, planning, memory, and alignment. Monitor and improve model performance post-deployment through data-driven iteration and error analysis. Contribute to technical discussions, model reviews, and best practices within the applied science community. 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. 2+ years of experience in applied machine learning, with a focus on LLMs, agent systems, or reinforcement learning. Solid hands-on experience with model training pipelines using PyTorch, TensorFlow, JAX, or similar frameworks. Familiarity with distributed training, prompt engineering, evaluation strategies, and model deployment best practices. Experience with retrieval-augmented generation (RAG), tool use, planning agents, or long-context modeling. Solid publication record (e.g., NeurIPS, ICLR, ACL, ICML, EMNLP) is a plus, but emphasis is placed on practical contributions. Solid coding and debugging skills, and comfort working in cross-functional, agile environments.