# Introduction
I am a Staff Software Engineer and Researcher at Meta, specializing in large-scale distributed systems and applied AI. I am passionate about building reliable, scalable, and intelligent infrastructure that powers the next generation of agentic workflows. With deep expertise spanning large-scale distributed systems, agentic infrastructure, systems architecture, and operational resilience, I focus on solving the hardest problems at the intersection of systems, AI, and real-world execution where theory meets engineering tradeoffs.

Within Meta SuperIntelligence Lab (MSL), I have contributed to building agentic infrastructure - systems where AI agents operate within structured distributed environments, interacting with monitoring, scheduling, and feedback loops. My work in this space focuses on:
- Evaluation and auditing of AI-driven decision systems in high-stakes production environments
- Reliability, safety, and human oversight in autonomous and semi-autonomous systems
- Designing feedback mechanisms to align system behavior with user and operational goals
- Measuring real-world impact beyond offline metrics

# Building Elastic Compute Infrastructure at Meta
I also built the next-generation of elastic compute infrastructure to increase overall fleet utilization responsible for managing ~30% of Meta’s capacity (tens of millions of servers) across ~20 geo-distributed datacenter saving billions of dollars in Capex. This also involved partnering with VPs across Ads/Whatsapp/IG/Finance/Infra to set multi-year roadmap and strategy for increasing fleet-wide efficiency.

# Research
At Meta, my recent research includes Dynamic Idle Resource Leasing to Safely Oversubscribe Capacity at Scale, where I designed and deployed a production system that improves datacenter utilization by leasing idle capacity while preserving reliability and strict SLO guarantees. This work required building rigorous evaluation frameworks spanning simulation, controlled experimentation, and real-world safety validation - balancing algorithmic optimization with operational risk. The system has delivered measurable infrastructure-efficiency gains at production scale. I have also authored papers with 90+ citations.

# What I care about
I think deeply about how distributed services communicate, self-coordinate, and act with reliability under ambiguity. My work is rooted in understanding latency, correctness, failure modes, and semantic interoperability - not just performance on benchmarks, but real-­world outcomes that matter in production. I’ve led teams and initiatives that:
- Architect complex distributed platforms that serve high-availability workloads at scale
- Design agentic systems and frameworks that enable coordinated autonomous behavior across services and models
- Build operationally robust infrastructure with strong observability, fault tolerance, and graceful degradation
- Translate cutting-edge research into developer-ready systems and patterns

# Education
I graduated from University of California, Los Angeles (UCLA) with a Master's in Computer Science in December 2019. At UCLA, my focus area was on building scalable distributed systems leveraging Machine Learning.

# Ways to collaborate:
• Keynotes, conference talks, and technical workshops
• Partnerships with AI platforms, developer tools, and education organizations
• Advisory and consulting on AI infrastructure and large-scale systems

For speaking, partnerships, or advisory inquiries: nishantgupta@g.ucla.edu