🛠 Tech Stack

💼 About This Role

You'll work at the intersection of engineering and applied AI research, contributing to post-training, synthetic data generation, and large-scale evaluation for frontier language models. Your work will directly impact how LLMs master tool use, agentic behavior, and real-world reasoning. You'll build scalable systems and run experiments that push the boundaries of model performance.

🎯 What You'll Do

• Work on post-training and RLVR pipelines to improve model performance.
• Design and run reward-shaping experiments and algorithmic improvements.
• Build and maintain data generation and augmentation pipelines.
• Create and refine rubrics, evaluators, and scoring frameworks.
• Build and operate LLM evaluation systems at scale.

📋 Requirements

• Strong applied research background in post-training or model evaluation.
• Strong coding proficiency with machine learning models.
• Hands-on experience with data structures and algorithms.
• Familiarity with APIs, SQL/NoSQL databases, and cloud platforms.

✨ Nice to Have

• Real-world post-training team experience in industry.
• Publications at NeurIPS, ICML, or ACL.
• Experience in synthetic data generation or RL-style workflows.

🎁 Benefits & Perks

• 📈 Generous equity grant vested over 4 years
• 🏠 $10K housing bonus if you live within 0.5 miles of office
• 🍽️ $1.5K monthly stipend for meals
• 💪 Free Equinox membership
• 🩺 Health insurance