Tech Lead, Research Scientist/Engineer - AI Infrastructure

What you'll do

• Design and evaluate end-to-end AI factory architectures, including technical trade-offs involving silicon, software, energy, and supply chains.
• Explore emerging AI systems, distributed training, hardware acceleration, and AI memory; build prototypes and share findings in technical reports.
• Benchmark and optimize performance across the ML stack, and coordinate solutions with 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; backgrounds in cognitive science, computational neuroscience, or psychology are also 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 and inference, heterogeneous compute, training clusters, or high-performance networking.
• Familiarity with AI memory systems, retrieval-augmented architectures, chip design, data-center energy and cooling, or AI hardware supply chains; publications or open-source contributions.

Tools

RDMA, NCCL, KV cache

AI focus

Research and development of AI infrastructure for training, RL, inference, and long-horizon agent memory and reasoning; integrates AI tools into research workflows.