ML Engineer, Inference Optimization

Build AI; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive pay

Medical coverage

Dental coverage

Vision coverage

Housing subsidy

Relocation assistance

Wellness benefits

Lunch provided

Compute budget

AI credits

Travel opportunities

Job summary

Build AI is hiring to cut inference costs and improve latency and throughput for scalable physical AI workloads. You will optimize serving, quantization, and kernel performance while collaborating with research and product teams to keep models accurate yet affordable at scale.

The role focuses on profiling, architectural tweaks, and cost-aware engineering in a small, cost-conscious team in a fully in-person setup across San Francisco and Shenzhen.

Qualifications

• Strong ML/systems engineer with real inference optimization experience (serving, compilers, CUDA/kernels, quantization).
• Comfortable in Python and in C++ or Rust for performance-critical paths.
• Think in dollars and tokens/frames per second, not only in accuracy tables.
• Familiarity with PyTorch (or JAX) and profiling tools.
• Comfortable in a small research team shipping under cost pressure.

Responsibilities

• Own inference performance: latency, throughput, and cost per unit of work (tokens, frames, or jobs).
• Reduce compute costs via kernels, batching, quantization, and efficient serving.
• Profile pipelines (Nsight, PyTorch Profiler, or equivalent) to identify bottlenecks and ship fixes.
• Collaborate with research and product to keep models affordable at scale.
• Build the serving and eval path so experiments don’t blow up the inference bill.
• Measure cost as a first-class metric.

Skills

Inference optimization

Python

C++ or Rust

PyTorch

Profiling tools

Tools

CUDA

Kernels

Job description

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
• 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

How we're different

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: [e-mail]