The work.
The Factory ships agents and retrieval onto client platforms. This role finds the structural waste in that path and removes it: end-to-end latency, memory bandwidth, GPU occupancy, and the bill for a token that did not need to be spent.
You will measure first. A change that cannot be scored against p99, throughput, or serving cost does not land. Accuracy is a constraint, not a thing we trade away to look fast.
What you will do
- Walk the critical path from an agentic loop to the inference pipeline and the container that holds it, and name the hop that is actually expensive.
- Build the telemetry and profiling the rest of the team can run: latency, memory bandwidth, compute throughput, GPU utilisation, serving cost on the clusters that already exist.
- Land architecture changes with the people who train, serve, and productise the models — fewer wasted tokens, tighter context handling, a topology that does not multiply RPCs for sport.
- Tune serve-side workloads: hardware utilisation, context pipelines, microservice shape. The number that moves is the one an operator can feel or a finance lead can read.
What you bring
- ML inference or serving platforms and large cloud estates in the same week: Kubernetes, distributed services, GPU orchestration.
- A habit of chasing latency and memory through a high-throughput system with traces, profiles and benchmarks you can replay.
- A production codebase that was messy when you arrived, and a win you can point at after — milliseconds or dollars, written down.
- The trade: raw speed against compute cost, reliability, and the person waiting on the token stream.
Useful, not required
- CUDA kernels, Triton, vLLM, TensorRT-LLM, or a C++ / Rust runtime you extended rather than wrapped.
- Agent loops, context-window management, streaming responses — how they fail when the KV cache or the network is the real budget.
- Open-source work on ML systems, HPC libraries, or cloud-native tooling.
What to send
- A CV and the two profile links the form asks for.
- One optimisation you owned: the bottleneck, the measurement, the change, and what it did to p99 or the serving bill.
The first quarter
- Audit latency and cost across inference, the network, and the agent loop.
- Establish one profiling baseline for latency, throughput, and GPU memory.
- Land a measured reduction in p99 latency or serving cost on a primary workload.