Staff AI Inference and Acceleration Engineer

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

Job summary

Figureai in San Jose, CA, is seeking a Staff AI Inference & Acceleration Engineer to own the on-board inference architecture for humanoid robots. The role involves mapping AI workloads efficiently across compute hardware while driving optimization to meet strict latency and reliability demands.

Successful candidates will have a strong background in hardware acceleration and ML systems, with at least 8 years of industry experience. Key responsibilities include optimizing models and defining inference architectures.

Qualifications

• 8+ years of experience in hardware acceleration and compute architecture.
• Deep understanding of AI/ML inference model formats and deployment pipelines.
• Hands-on experience optimizing embedded hardware models.

Responsibilities

• Own and define the on-board inference architecture for humanoid robots.
• Optimize inference toolchains for target hardware.
• Partner with teams to ensure hardware-friendly model architecture.

Skills

Hardware acceleration

ML systems

C++

Python

AI/ML inference

Computational architecture

Education

M.S. or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science or related field

Tools

TVM

MLIR

TensorRT

Torch

CUDA

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA.

We are looking for a Staff AI Inference & Acceleration Engineer to join the Platform Software team and own the on-board inference architecture for Figure’s humanoid robots. You will be the technical authority on how AI workloads are mapped, optimized, and executed across the robot’s compute hardware — driving down power consumption and cost while meeting the strict latency and reliability demands of a real-time autonomous system.

Responsibilities

• Own the on-board inference architecture — mapping models to available 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.

Requirements

• 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.

Bonus Qualifications

• Knowledge of real-time operating constraints and their impact on inference scheduling.
• Track record of co‑designing model architectures with ML teams to meet hardware constraints.

The US base salary range for this full-time position is between $180,000 - $275,000 annually.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.