Senior Research Scientist/Engineer - AI Infrastructure

What you'll do

• Design and evaluate architectures spanning compute, storage, networking, chips, power, data, and applications; assess energy and supply-chain constraints.
• Research emerging AI systems, distributed training, reinforcement learning, hardware acceleration, and AI memory; build prototypes and write technical reports.
• Benchmark and optimize performance across the ML stack, and coordinate infrastructure solutions across research, engineering, hardware, data-center, and product teams.

What they ask for

• Completing or recently completed a PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline; cognitive science, computational neuroscience, or psychology backgrounds are welcome with strong systems fundamentals.
• Experience in distributed systems, infrastructure engineering, or ML systems, including exposure to large-scale training or RL pipelines.
• Strong proficiency integrating AI tools into knowledge discovery and research workflows.
• Ability to learn quickly and communicate effectively across teams.

Nice to have

• Experience with large-scale model training/inference, heterogeneous AI compute, training clusters, HPC workloads, or training/evaluation data pipelines.
• Familiarity with AI memory or retrieval-augmented architectures; chip design, data-center energy/cooling, hardware supply chains, relevant publications, or open-source contributions.

Tools

RDMA, NCCL, KV cache

AI focus

AI infrastructure for large-scale training, RL, inference, and agent memory/reasoning systems; integrates AI tools into research and knowledge-discovery workflows.