Member of Technical Staff - Distributed Systems

🚨 Member of Technical Staff – Distributed Systems, Scheduling & AI Infrastructure

📍 San Francisco – Onsite

I’m working with a fast-growing AI infrastructure company building the orchestration layer for heterogeneous compute.

Its platform partitions, schedules and routes inference and agentic workloads across CPUs, GPUs and emerging accelerators—allowing customers to use diverse hardware without rebuilding their stack for every target.

Following an $80m Series A, the company has grown to approximately 25 people, generated eight figures in revenue and secured customers including a frontier AI lab and a hyperscaler.

This is a high-ownership distributed systems opportunity, joining a small, deeply technical team to build foundational infrastructure for production AI workloads.

You’ll work on:

⚙️ Building distributed schedulers, control planes and orchestration systems

🧠 Decomposing and placing inference and agentic workloads across heterogeneous hardware

🔄 Designing coordination, concurrency and resource-management systems spanning services, workers and compute nodes

🛡️ Solving difficult production challenges involving reliability, recovery and distributed failure modes

📊 Improving latency, throughput, memory usage, hardware utilisation and power efficiency

🚀 Owning foundational systems from ambiguous requirements through architecture, implementation and production operation

🤝 Working closely with Gimlet’s founders and engineers across distributed systems, compilers, runtimes, kernels and hardware

Requirements:

✅ Evidence of personally building and delivering a production control plane, scheduler, orchestration layer or similarly complex distributed system

✅ Deep understanding of concurrency, coordination, resource management and failure recovery

✅ Experience taking responsibility for production scale, reliability and difficult failure modes

✅ Clear personal ownership of architecture and core implementation—not simply operating infrastructure designed by others

✅ Ability to move quickly and make sound technical decisions with incomplete requirements and limited support

✅ Current hands-on implementation depth and a genuine desire to remain close to the code

⭐ Experience with ML serving, distributed compute, heterogeneous hardware, Kubernetes internals or production-quality open-source infrastructure is highly valuable

You’ll join a technically exceptional founding team with experience across NVIDIA, Google AI, Intel, Pixie Labs and Stanford, working on production infrastructure with real customers and measurable commercial demand.

The environment is collaborative, hands-on and deliberately low on bureaucracy. Engineers have direct access to the founders and the freedom to move across scheduling, runtimes, inference, compilers and hardware-level performance problems.

💰 Package: $150k–$350k, plus meaningful equity.