<div class="content-intro"><p class="p1">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.</p></div><p>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.</p> <p><strong>Responsibilities:</strong></p> <ul> <li> <p>Own the on-board inference architecture — mapping models to available accelerators (NPU, GPU, DSP, CPU) based on latency, power, and memory budgets.</p> </li> <li> <p>Partition inference workloads across heterogeneous compute resources, balancing real-time performance with power and thermal constraints.</p> </li> <li> <p>Define and maintain a system-level compute budget across all inference tasks running on the robot.</p> </li> <li> <p>Evaluate next-generation acceleration hardware and contribute to the definition of future compute platform requirements.</p> </li> <li> <p>Optimize inference toolchains end-to-end — from model export through runtime execution — for target hardware.</p> </li> <li> <p>Apply quantization (INT8, INT4, mixed-precision), pruning, operator fusion, and other compression techniques to reduce compute, memory, and power footprint.</p> </li> <li> <p>Profile inference pipelines to identify and eliminate bottlenecks in latency, memory bandwidth, and power consumption.</p> </li> <li> <p>Optimize kernel scheduling, memory layout, and data movement across the compute hierarchy.</p> </li> <li> <p>Partner closely with the AI/ML team to define model architecture constraints that are hardware-friendly from the outset.</p> </li> <li> <p>Work with the Platform Software team on runtime integration, scheduling, and power management.</p> </li> <li> <p>Engage with silicon vendors and research teams to track the accelerator landscape and influence hardware roadmaps.</p> </li> </ul> <p><strong>Requirements:</strong></p> <ul> <li> <p>M.S. or Ph.D. in Computer Engineering, Electrical Engineering, Computer Science, or a related field — or equivalent industry experience.</p> </li> <li> <p>At least 8 years of industry experience in hardware acceleration, ML systems, or compute architecture.</p> </li> <li> <p>Deep understanding of AI/ML inference — model formats (ONNX, TFLite, etc.), inference runtimes, and deployment pipelines.</p> </li> <li> <p>Hands-on experience optimizing models for edge or embedded hardware using quantization, pruning, and operator-level tuning.</p> </li> <li> <p>Strong understanding of computer architecture — memory hierarchies, data movement, and heterogeneous compute.</p> </li> <li> <p>Experience profiling and benchmarking inference workloads across CPU, GPU, NPU, DSP.</p> </li> <li> <p>Familiarity with low-level toolchains and compilation frameworks (e.g. TVM, MLIR, TensorRT, Torch, SNPE/QNN, JAX, CUDA, ROCm).</p> </li> <li> <p>Solid software engineering skills in C++ and Python.</p> </li> <li> <p>Strong cross-functional communication skills — able to work effectively across hardware, software, and AI/ML teams.</p> </li> </ul> <p><strong>Bonus Qualifications:</strong></p> <ul> <li> <p> Knowledge of real-time operating constraints and their impact on inference scheduling.</p> </li> <li> <p>Track record of co-designing model architectures with ML teams to meet hardware constraints.</p> </li> </ul> <p>The US base salary range for this full-time position is $180,000 to $275,000 per year.</p> <p>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. </p>