Figureai

Figure is an AI Robotics company 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. We are based in North San Jose, CA and require 5 days/week in-office collaboration. It’s time to build.
We are looking for a State Estimation Engineer to own the architecture, algorithm development, and calibration workflows for a next-generation data collection system. This system powers two core capabilities: low-latency real-time teleoperation of our humanoid robots and ultra-high-precision offline trajectory reconstruction for data collection and policy training. You will build and deploy dual-tier estimation pipelines and user-onboarding calibration routines that fuse heterogeneous sensor modalities to track full-body human kinematics and floating-base motion across dynamic tasks.
Key Responsibilities:
<ul>
<li>Design and implement dual-tier state estimation algorithms in modern C++: low-latency, real-time filters for streaming teleoperation and batch optimization/smoothing routines for high-accuracy offline dataset generation.</li>
<li>Own and develop subject-calibration procedures, designing rapid, intuitive routines to estimate individual body segment dimensions, joint offsets, and sensor-to-body extrinsics whenever a user equips the system.</li>
<li>Develop robust sensor fusion architectures combining spatial transforms, visual-inertial data, and inertial signals into full-body kinematic pose estimates.</li>
<li>Address spatiotemporal sensor calibration, dynamic environmental interference, and kinematic constraint enforcement on human skeletal models.</li>
<li>Develop techniques to extract useful information from compliant tactile sensing in the presence of large sensor deformation, stretching or folding.</li>
<li>Diagnose and understand limitations of existing hardware or designs and inform future design requirements.</li>
<li>Evaluate novel sensing modalities to inform future hardware designs.</li>
<li>Build diagnostic tooling, validation pipelines, and error analysis workflows to evaluate accuracy for both online and offline algorithms.</li>
</ul>
Requirements:
<ul>
<li>4+ years of experience building multi-sensor fusion and state estimation solutions for dynamic hardware systems.</li>
<li>Hands-on expertise with both real-time filtering techniques ((E)KFs, sliding-window estimators) and offline batch optimization tools (Factor Graphs, GTSAM, Ceres, Non-Linear Least Squares).</li>
<li>Proven capability to design fast, reliable calibration, zeroing, and alignment workflows for multi-sensor suites and kinematic models.</li>
<li>Deep mathematical foundation in 3D spatial kinematics, Lie groups (SE(3), SO(3)), forward/inverse kinematics, and constrained optimization.</li>
<li>Proven ability to write high-performance, modular C++ for embedded or edge computing platforms alongside Python for data analysis and visualization.</li>
</ul>
Bonus Qualifications:
<ul>
<li>Experience with low-latency streaming pipelines for teleoperation, haptics, or human-in-the-loop control systems.</li>
<li>Background in human biomechanics, skeletal tracking, or body-mounted telemetry systems.</li>
<li>Prior experience applying Machine Learning (ML) techniques to motion priors, trajectory smoothing, or learned state estimation/calibration.</li>
</ul>
The US base salary range for this full-time position is between $150,000 and $300,000 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.

State Estimation Engineer - Data Collection Systems

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

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