Head of Engineering

For decades, progress in complex computer systems relied solely on human ingenuity. At Glia AI, we are changing that paradigm. We are building Glia, an AI-based systems engineer that augments human experts to design and optimize the next generation of infrastructure. Glia isn’t just an assistant; it is a multi-agent system that mimics the R&D process of top-tier engineers—reasoning, experimenting, and analyzing to deliver high-quality results 10-100x faster than humans. We are turning weeks of manual systems engineering into hours of automated, high-performance output. The company was founded by MIT Professors and two of their recent PhDs, based on a decade of award-winning research at MIT CSAIL. Backed by $13.5M in VC and angel funding, we are now scaling our engineering organization to meet the demands of the AI era.

About the Role

We are looking for a dynamic, highly technical, hands-on Head of Engineering to lead our small but exceptional team of engineers. This is a “player-coach” role for a leader who still loves to dive into the design, architecture, and code with the team, but is equally passionate about (i) driving accountability, (ii) building a high-performance, truth-seeking, can-do culture, and (iii) delivering tangible business results. You will manage the development of our platform that optimizes demanding AI workloads. You will report to Glia AI’s co-founder and CEO, Mohammad Alizadeh, and also work closely with co-founder Hari Balakrishnan, playing a pivotal role in shaping Glia’s technical and organizational future.

Responsibilities

• Lead & scale: Manage and grow a highly productive team of systems and AI engineers, fostering a culture of disciplined rigor without bureaucratic friction.

• Drive execution: Ensure that research-led designs and experiments are implemented in a robust, production-faithful platform that handles complex processing pipelines and heterogeneous hardware infrastructure.

• Engineering execution: Own and execute the engineering roadmap, working closely with the Product and Business leadership of the company.

• Bridge the gap between research and platform: Our work spans the range from research on trustworthy agentic AI to scalable and reliable platform engineering.

Desired Expertise and Experience

• Systems intuition: You have a deep understanding of the entire stack, from model code and serving frameworks (Triton, vLLM) to distributed systems to hardware-specific runtime configurations.

• Robust agentic AI systems: You have experience with agentic AI and a strong desire to build these systems using software and systems engineering principles.

• Leadership depth: Experience managing small, high-performance teams in a fast-paced startup or advanced R&D environment. You know how to drive a team toward milestones while maintaining high software and system quality.

Technical excellence:

• Experience with one or more of cloud systems for AI workloads, GPU optimization, and performance engineering.

• Strong proficiency in Python and AI/ML frameworks (e.g., PyTorch).

• Expertise in or experience with agentic AI systems.

• Experience with heterogeneous hardware environments and AI/ML pipelines.

• Mission-driven: You believe AI is the ultimate collaborator for discovering new ideas in

• AI-driven systems and are excited to contribute to this mission.

Why Join Glia AI?

• Strong pedigree: Work alongside a team of renowned experts in AI-driven systems.

• Massive impact: Our technology has demonstrated the ability to achieve significant improvements in long-running engineering research and design tasks spanning performance engineering, model pipeline development, and cost optimization..

• Robust financials: Our seed round ($13.5M) closed in September 2025; we’re backed by top-tier VC firms (Pillar, E14, and Neotribe), as well as luminaries like Jeff Dean, Ion

• Stoica, Aparna Chennapragada, Nick McKeown, Srinivas Narayanan, and more. We are in the midst of multiple paid PoCs with marquee customers.

• Solving hard problems: We are solving the fundamental bottlenecks of the AI revolution: quality, performance, and cost.