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About
We are seeking a strong candidate who can operate end-to-end across model development and production integration-someone equally strong in (1) LLM training (domain-adaptive continual pretraining, post-training, preference optimization / RL such as GRPO-style methods), (2) agentic systems (tool schemas, multi-turn reliability, rubric- or verifier-based learning loops), and (3) deployment-aware optimization (latency/cost/reliability tradeoffs, evaluation harnesses, and iterative improvement from production signals). \n\nThe ideal candidate has a track record of turning LLM research into shipped capabilities, can partner effectively with product, infra, and foundation model teams, and can lead ambiguous cross-LOB initiatives from problem definition through execution and scaling. Experience building robust tooling around synthetic data generation, eval, and training pipelines for LLMs is strongly preferred, since this role is expected to raise the bar on both research velocity and production readiness.
BS/MS in a quantitative field, including Computer Science, Maths, Statistics, Physics, etc.\nProficient programming skills in Python\nHands-on experience working with deep learning toolkits such as Jax, Tensorflow or PyTorch\nProven track record in training or deployment of large models or building large-scale distributed systems\nDeep understanding of Deep Learning and Large Language Models (LLMs)\nNatural Language Processing
PhD in a quantitative field, including Computer Science, Maths, Statistics, Physics, etc.
Languages
- English
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