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Staff AI Inference and Acceleration Engineer

Figure· Platform Software
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San Jose, CA$180k - $275kfull-timePosted 3mo ago

About the role

Role owning the on-board AI inference architecture for Figure’s humanoid robots, defining how AI workloads are mapped, optimized, and executed across heterogeneous compute hardware to meet latency, power, and reliability requirements in 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.

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.

Skills

Required:AI/ML inference optimizationQuantization and pruningProfiling and benchmarkingSoftware engineering
Last updated Oct 7, 2026. Interested? Apply on the company site.

About Figure

The future of home help is here

Figure is the first-of-its-kind AI robotics company bringing a general purpose humanoid to life.

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