Morph (YC S23) Is Hiring Member of Technical Staff MorphFast Models Optimized for Coding AgentsMember of Technical Staff$175K - $350K•0.40%•San Francisco, CA, USJob typeFull-timeRoleEngineering, Mach
By Coderz Club · 2026-08-02 · Tags: tech
Morph (YC S23) Is Hiring Member of Technical Staff
MorphFast Models Optimized for Coding AgentsMember of Technical Staff$175K - $350K•0.40%•San Francisco, CA, USJob typeFull-timeRoleEngineering, Machine learningExperience6+ yearsVisaUS citizen/visa onlyConnect directly with founders of the best YC-funded startups.Apply to role ›Tejas BhaktaFounderTejas BhaktaFounderAbout the roleThe 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. About the interview2 day work trial About MorphMorph builds specialized code-generation models and serves them on a custom inference stack. Technical work involves autoresearch for kernels and custom speculative-decoding models. MorphFounded:2025Batch:S23Team Size:3Status:ActiveLocation:San Francisco FoundersTejas Bhakta FounderTejas Bhakta Founder
MorphFast Models Optimized for Coding AgentsMember of Technical Staff$175K - $350K•0.40%•San Francisco, CA, USJob typeFull-timeRoleEngineering, Machine learningExperience6+ yearsVisaUS citizen/visa onlyConnect directly with founders of the best YC-funded startups.Apply to role ›Tejas BhaktaFounderTejas BhaktaFounderAbout the roleThe 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. About the interview2 day work trial About MorphMorph builds specialized code-generation models and serves them on a custom inference stack. Technical work involves autoresearch for kernels and custom speculative-decoding models. MorphFounded:2025Batch:S23Team Size:3Status:ActiveLocation:San Francisco FoundersTejas Bhakta FounderTejas Bhakta Founder