🛠 Tech Stack

💼 About This Role

You'll design data collection and synthesis strategies for post-training, combining human feedback and synthetic data to guide model behavior. Your work directly bridges raw model intelligence and useful, safe, collaborative AI. This role blends fundamental research and practical engineering in a fast-paced environment.

🎯 What You'll Do

• Design data collection strategies for post-training using human feedback and synthetic data.
• Develop pipelines for scalable, high-quality human labeling and synthetic data generation.
• Iterate on evals: define metrics, optimize, and ensure they capture what matters.
• Design metrics and benchmarks for data quality and alignment impact.

📋 Requirements

• Strong engineering skills with ability to debug in complex codebases.
• Experience with data curation, human feedback, or synthetic data generation for LLMs.
• Ability to design, run, and interpret experiments with scientific rigor.
• Proficiency in Python and familiarity with a deep learning framework like PyTorch, TensorFlow, or JAX.

✨ Nice to Have

• Strong grasp of probability, statistics, and ML fundamentals.
• Prior experience with RLHF, RLAIF, preference modeling, or reward learning.
• Experience managing human data collection campaigns or large-scale annotation workflows.

🎁 Benefits & Perks

• 🏖️ Visa sponsorship available.
• 💰 Competitive compensation in the range $350k-$475k.

📨 Hiring Process

This is an evergreen role; applications are reviewed continuously and candidates may be reached out as opportunities open.

🚩 Heads Up

• Evergreen role with no guarantee of immediate opening.