Topic: distributed systems

Four-month, full-time research fellowship developing machine-learning infrastructure and reinforcement-learning methods. Projects can address training performance, distributed systems, synthetic-data and environment pipelines, or model generalization. Fellows receive researcher mentorship, a weekly stipend and benefits. Applications are reviewed on a rolling basis for the next cohort, expected in January 2027. San Francisco workspace access and remote participation from the United States, United Kingdom or Canada are available. A subsequent permanent role is not guaranteed.

Berkeley professor Ion Stoica discusses how research software becomes useful infrastructure and, sometimes, a company. The conversation draws on systems projects and companies associated with his laboratory to examine adoption, maintenance and the role of students. It also covers the complexity of the AI software stack, GPU utilization, coding agents in distributed systems, reward design and regulating outcomes. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Philip Kiely surveys inference engineering at the intersection of GPU programming, applied research and distributed systems. Topics include batching, quantisation, speculative execution, cache reuse and workload-specific serving, alongside the choices between hosted APIs, dedicated deployments and internal platforms for multimodal and agent applications.