Figureai

<div>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.</div>We are looking for a Supply Chain Analytics Intern to join our Global Supply Management (GSM) team. In summary, you will own the GSM team's supplier ramp and sourcing readiness toolset as we move from one prototype build to the next, and at the end of your term run the build kickoff readiness review off it.
Responsibilities:
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Own the sourcing status tracker: one record per part on the current and next build bills of materials showing awarded supplier, quote status, NDA and master supply agreement (MSA) status, and approved supplier list (ASL) qualification status. Refresh it weekly so GSMs and program management can see which parts have no supplier on paper.
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Own the tooling and non-recurring engineering (NRE) tracker: every GSM-owned tooling and NRE purchase orders, with kickoff, T1 sample, first article inspection (FAI), and payment milestone dates tracked against need dates.
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Own the build cost baseline and next-build cost targets: normalize quotes across suppliers, roll them up by sub-assembly for the current build, then set targets by sub-assembly for the next build. This becomes the baseline for the team's cost reduction tracking through future builds.
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Own the supplier capacity view: committed supplier capacity against the next-build forecast by part and by source, flagging where a supplier cannot meet demand before build kickoff. Support onboarding of new vendors by collecting NDA, MSA, and quality documents for GSM review.
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Pull supplier scorecard data for the GSM-run quarterly business reviews (QBRs) and build quote analysis packages that GSMs take into negotiations.
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At the end of the term, run the build kickoff readiness review off your toolset and document the handoff so the team can run it without you.
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Requirements: 
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Rising senior or recent graduate in Supply Chain Management, Industrial Engineering, Operations, Business, or a related field; must be graduating within one year.
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Available full time for the winter term, January through April 2027 (approximately 16 weeks).
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Advanced Excel (Power Query, lookups, pivots) and at least one of SQL or Python for pulling and joining data across systems.
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Working knowledge of hardware supply chain basics: bills of materials, supplier quotes, purchase orders, and tooling.
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Clear written communication; able to document a process so someone else can run it.
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Bonus Qualifications: 
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Prior internship or co-op in sourcing, procurement, or supply chain at a hardware company.
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Hands-on experience with an ERP (NetSuite preferred) or a BI tool such as Power BI or Tableau.
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Exposure to should-cost analysis, quote normalization, or cost reduction tracking on a physical product.
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The US base pay range for this full-time position is $40.00 to $45.00 per hour.
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.

Supply Chain Analytics Intern [Winter 2027]

<div>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.</div>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:
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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.
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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.
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Develop robust sensor fusion architectures combining spatial transforms, visual-inertial data, and inertial signals into full-body kinematic pose estimates.
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Address spatiotemporal sensor calibration, dynamic environmental interference, and kinematic constraint enforcement on human skeletal models.
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Develop techniques to extract useful information from compliant tactile sensing in the presence of large sensor deformation, stretching or folding.
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Diagnose and understand limitations of existing hardware or designs and inform future design requirements.
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Evaluate novel sensing modalities to inform future hardware designs.
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Build diagnostic tooling, validation pipelines, and error analysis workflows to evaluate accuracy for both online and offline algorithms.
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Requirements:
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4+ years of experience building multi-sensor fusion and state estimation solutions for dynamic hardware systems.
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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).
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Proven capability to design fast, reliable calibration, zeroing, and alignment workflows for multi-sensor suites and kinematic models.
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Deep mathematical foundation in 3D spatial kinematics, Lie groups (SE(3), SO(3)), forward/inverse kinematics, and constrained optimization.
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Proven ability to write high-performance, modular C++ for embedded or edge computing platforms alongside Python for data analysis and visualization.
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Bonus Qualifications:
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Experience with low-latency streaming pipelines for teleoperation, haptics, or human-in-the-loop control systems.
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Background in human biomechanics, skeletal tracking, or body-mounted telemetry systems.
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Prior experience applying Machine Learning (ML) techniques to motion priors, trajectory smoothing, or learned state estimation/calibration.
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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.

State Estimation Engineer, Data Collection Systems

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

Figureai

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Supply Chain Analytics Intern [Winter 2027]

State Estimation Engineer, Data Collection Systems

Staff AI Inference and Acceleration Engineer

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