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.

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
• 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
• 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.