Member of Technical Staff, Post-Training
Core
Design and build post-training systems, evaluation frameworks, and data pipelines for frontier AI models.
Role type
Senior IC machine learning engineer (post-training & evaluation)
Builds
Evaluation frameworks, benchmarks, training environments, data-processing pipelines, and quality-control systems for frontier models.
Domain
Artificial Intelligence / Machine Learning / Frontier Model Development
Deliverable
production ML models
Required skills
Post-training methodologies (SFT, RLHF, DPO, PPO, reward modeling), Python, PyTorch, experimental design, system design, data pipeline engineering, hypothesis formulation, signal-to-noise analysis
Preferred skills
Large-scale ML training/inference systems, LLM/agent benchmarking, reinforcement learning, alignment research, synthetic data, human-in-the-loop systems, open-source contributions, technical leadership
Technologies
PyTorch, Python
Responsibilities
Design post-training systems and methodologies; translate research needs into experiments and production implementations; build evaluation frameworks and data pipelines; run iteration loops to prototype and evaluate; partner with researchers to develop high-signal data methods; productize repeatable patterns into reusable platforms; mentor team members and contribute to the field via benchmarks or research.
Seniority
Senior, hands-on IC
