The best candidates would be top 1% at multiple parts of the inference stack.
work on PD disaggregation research
Morph builds the inference infrastructure behind the fastest open models. Our stack spans kernels, model serving, routing, autoscaling, and capacity. We are hiring a performance engineer to make the entire system faster, cheaper, and more reliable.
### 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
* Build benchmarks and observability that make bottlenecks obvious
* Validate that every optimization preserves model quality and correctness
* Stack-rank opportunities and ship the highest-impact fixes yourself
### You might be a fit if you
* Have optimized complex production systems
* Understand GPU performance, memory bandwidth, collectives, and inference serving
* Are strong in Python and comfortable navigating unfamiliar codebases
* Can turn profiling data into clear engineering decisions
* 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.
Visa sponsorship is not available for this role.
