Senior Research Scientist, Post-Training

About the Role

Join an applied AI research team developing post-training methods for large language models and other foundation models. Your work will help improve how models reason, make decisions, and perform in complex tasks.

What You'll Do

• Research and implement reinforcement learning and other post-training techniques for transformer-based models.

• Design and run machine learning experiments, using rigorous evaluation methods to measure model performance.

• Build and optimize training workflows, including systems that support distributed training at scale.

• Apply expert reasoning data and evaluation approaches to improve model behavior across challenging tasks.

• Share research findings through publications or by delivering reliable machine learning systems.

What We're Looking For

• At least 3 years of experience in machine learning research, post-training, reinforcement learning, or related model optimization work.

• Experience implementing reinforcement learning algorithms or post-training techniques in research or production settings.

• Hands-on experience with large language models or transformer architectures, plus a track record of research publications or shipped machine learning systems.

• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.

• Experience designing rigorous experiments and evaluations, and working with distributed training or scalable machine learning systems.

• A PhD in machine learning, computer science, or a related field, or equivalent research experience; open-source contributions are a plus.

Compensation & Benefits

Annual salary range: $150,000 to $250,000 USD. Visa sponsorship is available.

Location

This is an on-site role based in San Francisco, California, United States.