Morph was a 1 person company from 0 → 10M of revenue. We will be the first 10 person $10b company. Every employee should contribute >30M of revenue/yr to the company. The best candidates would be top 1% at multiple parts of the inference stack, yet have breadth across the whole stack. Morph builds high margin inference infrastructure. Our stack spans kernels, model serving, routing, autoscaling, and capacity.
### What you’ll do
- Find the gap between theoretical hardware performance and production performance
- Trace latency and throughput regressions from the API layer down to individual kernels
- Optimize batching, scheduling, routing, quantization, and distributed execution
- Work on new research directions around caching
- Work with NVLink and RoCE
- Validate that every optimization preserves model quality and correctness
### You might be a fit if you
- Have optimized complex production systems
- Can juggle 8+ Codex/Claude/other coding agents concurrently
- Understand GPU performance, memory bandwidth, collectives, and inference serving
- Are strong in Python, CuTEdsl, and comfortable navigating unfamiliar codebases
- Care about tokens per second, tokens per dollar, and correctness equally
You will work directly with the founders on problems that determine how efficiently frontier-scale models can be served. Small team, enormous compute, immediate production impact.