AIML - Machine Learning Researcher, Post-Training for Foundation Models

What you'll do

• Design and iterate on post-training recipes combining SFT, reinforcement learning, and reasoning approaches.
• Develop algorithms for preference optimization, model steering, and safety, and research data generation and filtering methods.
• Build evaluations of model helpfulness, factuality, and utility; collaborate with pre-training and product teams.

What they ask for

• Demonstrated deep-learning expertise focused on LLMs, post-training, or reinforcement learning, supported by publications or relevant real-world accomplishments.
• Programming proficiency in Python and a deep-learning framework such as JAX or PyTorch.
• PhD or equivalent practical experience in Computer Science, Machine Learning, or a related technical field.

Nice to have

• Track record in foundation-model post-training, data generation and quality assessment, or evaluation methodologies.
• Experience improving reasoning, training large models at scale, or handling distributed-training challenges.

Pay

$184,700 and $324,800 USD base salary

Tools

Python, JAX, PyTorch

AI focus

Foundation-model post-training, including supervised fine-tuning, reinforcement learning, reasoning, preference optimization, safety, reward modeling, data generation and filtering, and evaluation.