Staff AI Inference & Acceleration Engineer

Figure; California City (CA); On-site; USD 180,000 - 275,000; Full time

Job summary

Figure is seeking a Staff AI Inference & Acceleration Engineer to own the on-board inference architecture for its humanoid robots. You will map models to accelerators (NPU, GPU, DSP, CPU) to meet latency and power targets, while balancing memory budgets and real-time constraints.

You will optimize end-to-end toolchains, apply quantization and pruning, and profile pipelines to remove bottlenecks. Collaboration with AI/ML and Platform Software teams is essential.

Qualifications

• MS/PhD or equivalent industry experience.
• 8+ years 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 (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 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 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 with the AI/ML team to define model architecture constraints 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 accelerator landscape and influence hardware roadmaps.

Skills

C++

Python

Hardware acceleration

ML systems

Edge inference

Quantization

Profiling

Education

MS/PhD in Computer Engineering, Electrical Engineering, Computer Science, or related field

Tools

TVM

MLIR

TensorRT

Torch

SNPE/QNN

CUDA

ROCm

Job description

Figure is seeking a Staff AI Inference & Acceleration Engineer to own the on-board inference architecture for its humanoid robots. You will map models to accelerators (NPU, GPU, DSP, CPU) to meet latency and power targets, while balancing memory budgets and real-time constraints.

You will optimize end-to-end toolchains, apply quantization and pruning, and profile pipelines to remove bottlenecks. Collaboration with AI/ML and Platform Software teams is essential.