ABOUT THE COMPANY
We're building autonomous research agents for recursive self-improvement (multi-agent systems that propose, run, and analyze machine learning experiments). We're a small team based in San Francisco, on-site
ABOUT THE ROLE
You build and operate the inference systems that serve our models in production. The work spans serving infrastructure, runtime optimization, and the long tail of production infrastructure that come with running real workloads.
This is an engineering role, not a research role. You'll measure, profile, debug, and ship. You'll work alongside researchers, but your job is to make their work fast and reliable in production. Real ownership, real autonomy.
WHAT YOU'LL DO
Build, operate, and harden production inference systems serving large models at high throughput
Own the performance characteristics of those systems end-to-end: throughput, latency, cost-per-token, reliability under load
Profile real workloads to identify bottlenecks; ship fixes that move the metric you set out to improve
Implement and integrate inference optimizations from the research team (quantization, custom kernels, scheduling improvements, memory management) into production
Design observability into the inference layer: metrics, tracing, alerting that surface regressions before users notice them
Run capacity planning, autoscaling, and load testing for varied workload shapes (batch, online, mixed, agentic)
Diagnose and resolve production incidents; write postmortems that turn bugs into systemic fixes
WHAT WE'RE LOOKING FOR
Senior ML systems engineer with 3+ years building production-grade, large-scale serving infrastructure
Strong distributed systems experience ; you've been on-call for systems that matter
Performance profiling and optimization fluency: you read flame graphs, you are analytical and measured before you change
Experience with GPU-accelerated inference at scale (multi-GPU, multi-node, batched and streaming workloads), preferably experience with AMD GPUs
Fluent Python; comfortable reading and writing systems-level code in at least one of the following languages: C++,CUDA, ROCm or Triton
Track record of shipping production infrastructure, preferably surfaces serving millions of requests across diverse workloads
Good written communication; you can write a runbook that someone else can follow at 3am
NICE TO HAVE
Open-source contributions to inference / serving frameworks
Experience with mixed cloud and on-premises deployments
Familiarity with hardware-aware optimization (memory hierarchy, NCCL/RDMA, NUMA)
Background in compilers, runtimes, or accelerator software stacks
THIS ROLE IS PROBABLY NOT FOR YOU IF
You're primarily a researcher, the work here is building, not exploring
You want to focus narrowly on one component; this role spans the stack
Production responsibility (incidents, on-call, ownership of running systems) isn't appealing
Skills Required
- 3+ years building production-grade, large-scale model serving infrastructure
- Strong distributed systems experience and on-call experience for production systems
- Performance profiling and optimization fluency (able to read flame graphs and identify bottlenecks)
- Experience with GPU-accelerated inference at scale (multi-GPU, multi-node, batched and streaming workloads)
- Preferably experience with AMD GPUs
- Fluent Python
- Comfortable reading and writing systems-level code in at least one: C++, CUDA, ROCm, or Triton
- Track record of shipping production infrastructure serving high-volume, diverse workloads
- Ability to design observability (metrics, tracing, alerting) and write clear runbooks
- Willingness to own production responsibility (incidents, postmortems, on-call)
- Good written communication
- Open-source contributions to inference/serving frameworks
- Experience with mixed cloud and on-premises deployments
- Familiarity with hardware-aware optimization (memory hierarchy, NCCL/RDMA, NUMA)
- Background in compilers, runtimes, or accelerator software stacks
What We Do
MakerMaker.AI is an innovative company building autonomous research agents focused on recursive self-improvement. They develop sophisticated multi-agent systems that can independently propose, run, and analyze complex machine learning experiments. By creating agents that have the ability to build other agents, MakerMaker.AI seeks to accelerate the development of artificial intelligence and push the boundaries of autonomous research in machine learning.
