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 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.

Responsibilities:

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.

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.

Develop robust sensor fusion architectures combining spatial transforms, visual-inertial data, and inertial signals into full-body kinematic pose estimates.

Address spatiotemporal sensor calibration, dynamic environmental interference, and kinematic constraint enforcement on human skeletal models.

Develop techniques to extract useful information from compliant tactile sensing in the presence of large sensor deformation, stretching or folding.

Diagnose and understand limitations of existing hardware or designs and inform future design requirements.

Evaluate novel sensing modalities to inform future hardware designs.

Build diagnostic tooling, validation pipelines, and error analysis workflows to evaluate accuracy for both online and offline algorithms.

Requirements:

4+ years of experience building multi-sensor fusion and state estimation solutions for dynamic hardware systems.

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).

Proven capability to design fast, reliable calibration, zeroing, and alignment workflows for multi-sensor suites and kinematic models.

Deep mathematical foundation in 3D spatial kinematics, Lie groups (SE(3), SO(3)), forward/inverse kinematics, and constrained optimization.

Proven ability to write high-performance, modular C++ for embedded or edge computing platforms alongside Python for data analysis and visualization.

Bonus Qualifications:

Experience with low-latency streaming pipelines for teleoperation, haptics, or human-in-the-loop control systems.

Background in human biomechanics, skeletal tracking, or body-mounted telemetry systems.

Prior experience applying Machine Learning (ML) techniques to motion priors, trajectory smoothing, or learned state estimation/calibration.

The US base salary range for this full-time position is $150,000 to $300,000 per year.

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.