Member of Technical Staff — RL Research
Own RL and post-training infrastructure for omni foundation models. Build and scale rollout, reward, and policy systems from 0→1 for real-time audiovisual AI.
Lead optimization research applying large-scale constrained optimization and ML to real-time trading decisions. Requires 5-10+ years experience, strong math/ML background, production coding skills, and PhD-level coursework.
Own RL and post-training infrastructure for omni foundation models. Build and scale rollout, reward, and policy systems from 0→1 for real-time audiovisual AI.
Own and scale the distributed training infrastructure for large-scale omni model pretraining across GPU clusters, covering job orchestration, parallelism, GPU communication, data loading, and performance optimization.
Staff MLOps Engineer responsible for the reliability, performance, and cost-efficiency of production ML systems. Architect ML platform with feature stores, model registries, and automated CI/CD pipelines.
Build and own validation pipelines, CI/CD infrastructure, and platform integrations to launch frontier models and inference features reliably across AWS, GCP, and Azure. Requires strong large-scale distributed systems experience and track record improving release velocity.
Build and maintain distributed inference systems serving Claude to millions of users. Design intelligent routing, autoscaling, and high-performance infrastructure across diverse AI accelerators.