Machine Learning Scientist, GenAI & ML Frameworks

What you'll do

• Perform domain-adaptive continual pretraining and post-training for large language models.
• Implement preference optimization and reinforcement learning methods like GRPO-style training.
• Develop agentic systems focusing on tool schemas, multi-turn reliability, and verifier-based learning loops.
• Optimize models for production by balancing latency, cost, and reliability tradeoffs.
• Build robust tooling for synthetic data generation, evaluation harnesses, and training pipelines.
• Translate LLM research into shipped, production-ready features at scale.
• Lead cross-functional initiatives from initial problem definition through execution and scaling.

What we're looking for

• BS/MS in a quantitative field such as Computer Science, Maths, Statistics, or Physics.
• PhD in a quantitative field such as Computer Science, Maths, Statistics, or Physics (preferred).
• 3+ years of relevant work experience.
• Proficient programming skills in Python.
• Hands-on experience with deep learning toolkits such as Jax, Tensorflow, or PyTorch.
• Proven track record in training or deployment of large models or building large-scale distributed systems.
• Deep understanding of Deep Learning, Large Language Models (LLMs), and Natural Language Processing.
• Experience building robust tooling around synthetic data generation, evaluation, and training pipelines for LLMs (preferred).