Sr. Model Engineer, Post-Training & RL

Job summary

Responsibilities

You will work on the most critical post-training and reinforcement learning challenges at any given time — including reward modeling, preference optimization (RLHF/DPO), and RL for improving reasoning, truthfulness, and real-world capabilities.

• Design and run rigorous experiments that turn research ideas into measurable gains in model capability
• Improve model recipes through data quality, deduplication, and mixture experiments
• Define what makes RL data and rewards good, including grader design and preventing reward hacking
• Advance training science, from architecture and scaling experiments to post-training recipes for coding and computer-use agents
• Build evals and targeted tasks that expose model weaknesses and guide where the team invests next

What they're looking for

• Experience writing production-quality code in Python, PyTorch, JAX, Rust, or C++
• Experience training large models (e.g., pre-training, post-training, fine-tuning, or RL training of neural networks with 1B+ parameters)
• You believe truth-seeking AI is the most important and challenging problem
• You are obsessed about building incredibly useful models through post-training and RL techniques
• You are a power user of AI models and eager to push the boundaries of what’s possible with reinforcement learning and alignment methods

Tech stack

• Python
• PyTorch
• JAX
• Rust
• C++