Helix AI Engineer, Android

Figure AI Figure AI · Robotics · HQ · AI - Helix Team

Figure AI is seeking a Senior Android Engineer to build the mobile interface for their humanoid robots. This role involves low-level Android systems, NDK, real-time sensor/video pipelines, and on-device AI inference at the edge. The engineer will architect high-throughput data ingestion, implement custom HALs, optimize edge processing under constraints, and integrate AI inference libraries.

What you'd actually do

  1. Build and own the Android application that serves as the primary mobile interface to Figure's humanoid robots, connected via USB Host / Android Open Accessory protocols.
  2. Architect high-throughput, zero-drop data ingestion pipelines for high-FPS image sensors and high-frequency IMU data, using zero-copy memory techniques and real-time concurrency models.
  3. Implement custom hardware abstraction layers (HAL) and leverage the Android NDK (C/C++) for high-performance, low-latency processing.
  4. Optimize CPU/GPU workloads for real-time edge filtering under strict thermal and battery constraints, using foreground services and WorkManager for bulletproof background operation.
  5. Integrate on-device AI inference libraries (TFLite, MediaPipe, ONNX Runtime, OpenCV) for real-time computer vision and sensor fusion.

Skills

Required

  • Android NDK (C/C++)
  • custom HAL development
  • USB Host/AOA protocol communication
  • direct hardware interfacing
  • real-time data pipelines
  • low-latency processing
  • zero-copy memory
  • real-time concurrency
  • Android system resource management
  • CPU/GPU workload optimization
  • thermal and battery constraints
  • foreground services
  • WorkManager
  • C/C++ (NDK)
  • Kotlin/Java
  • shipping production Android applications
  • shipping and maintaining production Android applications at scale

Nice to have

  • integrating on-device CV/ML inference: TensorFlow Lite, MediaPipe, ONNX Runtime, or OpenCV
  • WebRTC
  • DSP techniques
  • robotics companion apps
  • industrial Android devices
  • AR/computer vision mobile apps
  • automotive HMI
  • drone control applications

What the JD emphasized

  • deep expertise in low-level Android systems
  • NDK
  • real-time sensor and video pipelines
  • on-device AI inference at the edge
  • below the Java/Kotlin layer
  • writing C/C++ via the NDK
  • implementing custom HALs
  • building zero-copy sensor pipelines
  • Deep expertise in Android NDK (C/C++)
  • custom HAL development
  • USB Host/AOA protocol communication
  • direct hardware interfacing below the standard SDK layer
  • real-time, low-latency data pipelines for high-bandwidth sensors
  • zero-copy memory
  • real-time concurrency
  • synchronization with zero frame drops
  • Android system resource management
  • CPU/GPU workload optimization
  • thermal and battery constraints
  • foreground services
  • WorkManager
  • shipping production Android applications in hardware-connected, latency-critical environments
  • shipping and maintaining production Android applications at scale

Other signals

  • shipping AI models
  • real-time inference
  • edge AI