ML Engineer

About Build AI

Build AI is the data hyperscaler for Physical AI. We're vertically integrated across hardware, manufacturing, logistics, collection, and model training to scale the physical labor dataset orders of magnitude faster than anyone in the world.

Job Summary

Inference is about 90% of compute spend. Economics are heavily driven by inference optimization. We’re hiring someone to make inference cheaper, faster, and good enough that we can scale the data engine and the product without the GPU bill eating the company.

Key Responsibilities

• Own inference performance: latency, throughput, and cost per unit of work (tokens, frames, or jobs)

• Cut the 90% compute line: kernels, batching, quantization, compilation, serving, and hardware utilization

• Profile pipelines (Nsight, PyTorch Profiler, or equivalent), find the real bottleneck, and ship the fix

• Work with research and product so models that are accurate are also affordable to run at scale

• Build the serving and eval path so experiments don’t hide the inference bill

• Measure cost as a first-class metric, not an afterthought once quality is “done”

You may be a good fit if you have (Must-have qualifications)

• Strong ML / systems engineer with real inference optimization experience (serving, compilers, CUDA/kernels, quantization, or similar)

• Comfortable in Python and in C++ or Rust for performance-critical paths

• You think in dollars and tokens/frames per second, not only in accuracy tables

• Familiarity with PyTorch (or JAX) and with profiling tools

• Comfortable in a small research team shipping under cost pressure

Strong candidates may also have experience with (Nice-to-have qualifications)

• CUDA, kernels, compilers (TVM, MLIR, TensorRT), or quantization in production

• You have owned GPU/accelerator cost as a first-class metric

• Serving stacks for video or large models

• Understanding of memory hierarchy, data movement, and low-precision compute

Benefits

• Competitive pay