diya gangwar | Infra AI Engineer

AI Startup

San Francisco, CAOn-siteFull-time$140k – $220k / year

About this role

Diya Gangwar Software Engineer | Machine Learning & Infra | CS @ Harvey Mudd San Francisco, California, United States (US) 500 connections • 2,334 followers About Software engineer focused on ML infrastructure, backend systems, and deploying AI in real-world environments. I’ve built LLM-powered tools, distributed training systems, and data pipelines across startups, research labs, and enterprise teams, always with an emphasis on reliability and scale. Drawn to problems where machine learning meets systems design. Total Experience: 2 years and 7 months Experience Infra AI Engineer - Zipline AI(https://www.linkedin.com/company/ziplineai) (Current) Jun 2026 - Present (1 month) in San Francisco Bay Area Department: Engineering and Technical • Level: Specialist Zipline AI is a Data Infrastructure and Analytics company. Zipline AI is a next generation data platform for AI/ML, working with a select group of companies to improve the way they ship ai/ml products. Zipline AI has 10-20 employees (+40% YoY). Headquartered in Union City, California, United States. Has $7M in total funding. AI ML Infra Engineer (Team Lead) - Applied Computing Research Labs(https://www.linkedin.com/company/applied-computing-research-labs) Aug 2025 - May 2026 (9 months) in Claremont, California, United States - Built Kubernetes-based ML training arena (SimKube) for RL benchmarking of cluster management agents - Engineered distributed training infrastructure (AWS EC2, S3) to scale large-scale experiments - Designed evaluation pipelines and sandboxed frameworks for safe ML agent interaction with live clusters Department: Engineering and Technical • Level: Head Applied Computing Research Labs is a Technology, Information and Internet company. ACRL is a company that provides open-source research and development in distributed systems. They apply simulation analysis to compute platforms to eliminate bottlenecks, optimize costs, and improve resource utilization without

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