Research Engineer, Core ML

📋 Description

• Translate RL algorithms, scheduling, and inference optimizations into production systems.
• Ship improvements in latency, throughput, and model quality at scale.
• Own end-to-end systems from algorithms to serving stacks and infrastructure.
• Work on high-performance inference and RL/post-training engines.
• Collaborate across RL, training, and deployment to ship robust frontier models.
• Lead technical initiatives and contribute to cross-team roadmaps.

🎯 Requirements

• Bias toward implementation and shipping; eager to modify real engines and services.
• Systems-first profile: large-scale inference systems, GPU performance, distributed serving.
• RL-first profile: RL/post-training for LLMs or large models.
• Model architecture design for Transformers or other large nets.
• Strong Python coding and ability to optimize across GPU, networking, memory.
• Track record of ML systems work; can read new RL/post-training papers.

🎁 Benefits

• Competitive compensation, startup equity, health insurance, and benefits.