Staff AI Inference Architect for Humanoid Robotics

Figure AI; San Jose (CA); On-site; USD 180,000 - 275,000; Full time

Job summary

Figure AI, based in San Jose, CA, seeks a Staff AI Inference & Acceleration Engineer to own the on-board inference architecture for humanoid robots. You will map models to accelerators, balance latency vs. power, and drive compute budgets for real-time systems.

You will optimize toolchains end-to-end, apply quantization and pruning, profile workloads, and collaborate with AI/ML teams to ensure hardware-friendly model constraints.

Qualifications

• M.S. or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or a related field — or equivalent industry experience.
• At least 8 years of industry experience in hardware acceleration, ML systems, or compute architecture.
• Deep understanding of AI/ML inference — model formats (ONNX, TFLite, etc.), inference runtimes, and deployment pipelines.
• Hands-on experience optimizing models for edge or embedded hardware using quantization, pruning, and operator-level tuning.
• Strong understanding of computer architecture — memory hierarchies, data movement, and heterogeneous compute.
• Experience profiling and benchmarking inference workloads across CPU, GPU, NPU, DSP.
• Familiarity with low-level toolchains and compilation frameworks (e.g. TVM, MLIR, TensorRT, Torch, SNPE/QNN, JAX, CUDA, ROCm).
• Solid software engineering skills in C++ and Python.
• Strong cross-functional communication skills — able to work effectively across hardware, software, and AI/ML teams.

Responsibilities

• Own the on-board inference architecture — mapping models to accelerators (NPU, GPU, DSP, CPU) based on latency, power, and memory budgets.
• Partition inference workloads across heterogeneous compute resources, balancing real-time performance with power and thermal constraints.
• Define and maintain a system-level compute budget across all inference tasks running on the robot.
• Evaluate next-generation acceleration hardware and contribute to the definition of future compute platform requirements.
• Optimize inference toolchains end-to-end — from model export through runtime execution — for target hardware.
• Apply quantization (INT8, INT4, mixed-precision), pruning, operator fusion, and other compression techniques to reduce compute, memory, and power footprint.
• Profile inference pipelines to identify and eliminate bottlenecks in latency, memory bandwidth, and power consumption.
• Optimize kernel scheduling, memory layout, and data movement across the compute hierarchy.
• Partner closely with the AI/ML team to define model architecture constraints that are hardware-friendly from the outset.
• Work with the Platform Software team on runtime integration, scheduling, and power management.
• Engage with silicon vendors and research teams to track the accelerator landscape and influence hardware roadmaps.

Skills

C++

Python

Hardware acceleration

ML inference

Profiling & benchmarking

Cross-functional communication

Edge/embedded optimization

ONNX/TFLite formats

Education

Master's degree or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or related field

Tools

TVM

MLIR

TensorRT

Torch

SNPE/QNN

CUDA

JAX

ROCm

Job description

Figure AI, based in San Jose, CA, seeks a Staff AI Inference & Acceleration Engineer to own the on-board inference architecture for humanoid robots. You will map models to accelerators, balance latency vs. power, and drive compute budgets for real-time systems.

You will optimize toolchains end-to-end, apply quantization and pruning, profile workloads, and collaborate with AI/ML teams to ensure hardware-friendly model constraints.