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 SummaryInference 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 ResponsibilitiesOwn 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”
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
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
Competitive pay
Medical, dental, and vision packages with generous premium coverage
$500 per month credit for waiving medical benefits
Housing subsidy of $2k per month for those living within walking distance of the office
Relocation support for those moving to San Francisco (Financial District) or Shenzhen (Nanshan)
Various wellness benefits covering fitness, mental health, and more
Daily lunch and dinner in our office
Unlimited compute budget subject to ROI justification
Unlimited Codex and Claude credits
Travel
Build believes in the Bitter Lesson. By taking a general approach of learning from humans, our addressable market is all physical labor.
We are a fully in-person team in San Francisco (Financial District) and Shenzhen (Nanshan), and greatly value engineering skills. We do not have boundaries between engineering and research, and we expect all of our technical staff to contribute to both and work across disciplines as needed.
Build AI is an equal opportunity employer. We review every application. If you do not meet every bullet, still apply. Questions: [email protected]
Skills Required
- Strong machine-learning and systems engineering experience with inference optimization, including serving, compilers, CUDA/kernels, quantization, or similar
- Proficiency in Python
- Proficiency in C++ or Rust for performance-critical paths
- Familiarity with PyTorch or JAX
- Familiarity with profiling tools
- Ability to work in a small research team and ship under cost pressure
- Production experience with CUDA, kernels, compilers such as TVM, MLIR, or TensorRT, or quantization
- Experience owning GPU or accelerator cost as a first-class metric
- Experience with serving stacks for video or large models
- Understanding of memory hierarchy, data movement, and low-precision compute
What We Do
Build AI is a public benefit corporation and data hyperscaler for Physical AI. It integrates hardware manufacturing, logistics, data collection, and model training to scale egocentric physical-labor datasets for researchers and labs. Its mission is to solve physical labor and unlock human potential, advancing robotics and physical superintelligence. The company operates in San Francisco and Shenzhen and develops economically useful human-data infrastructure.
