Staff Systems Engineer - Perception

Apptronik Apptronik · Robotics · HQ · Systems & Validation

Staff Systems Engineer for a human-centered robotics company focused on embodied AI. The role involves owning the system-level architecture, requirements, and performance of the robot's perception suite, acting as a liaison between autonomy software and hardware teams. Responsibilities include defining system architecture, leading trade studies, managing budgets (latency, compute, FOV), eliciting and managing requirements, authoring Interface Control Documents, architecting the V&V strategy, and ensuring functional safety compliance (ISO 13849 / IEC 61508). Requires deep expertise in perception modalities, autonomy familiarity, systems rigor, and analytical skills for modeling.

What you'd actually do

  1. Define the top-level architecture for the robot's perception system, balancing the needs of Visual-Inertial Odometry (VIO), manipulation, obstacle avoidance, and remote teleoperation.
  2. Lead data-driven trade studies to adjudicate complex architectural decisions (e.g., Stereo Vision vs. LiDAR for close-range manipulation, edge-compute vs. centralized processing, global vs. rolling shutter).
  3. Own and manage critical system budgets, including photon-to-action latency, compute utilization (TOPS), memory bandwidth, and spatial Field of View (FOV) coverage.
  4. Elicit, define, and manage perception system requirements using enterprise tools (e.g., Jama, Polarion, or DOORS). Decompose product-level goals into strict hardware and software specifications.
  5. Architect the master Verification and Validation plan for the perception stack. Define the KPIs and test methodologies required to prove that the system meets its requirements in real-world, dynamic environments.

Skills

Required

  • Systems Engineering for complex electromechanical systems, autonomous vehicles, aerospace, or advanced robotics
  • Deep understanding of modern perception modalities (Cameras, LiDAR, Radar, ToF, IMU) and the fundamental physics, error models, and integration challenges of each
  • Strong working knowledge of how perception data is consumed by downstream algorithms (SLAM, object detection, kinematic planning, sensor fusion)
  • Proven track record of managing complex requirements traceability and V&V matrices for shipped products
  • Ability to build mathematical models in Python or MATLAB to simulate system-level latency, FOV coverage, or optical performance to inform early-stage architecture
  • BS, MS, or PhD in Systems Engineering, Robotics, Electrical Engineering, Computer Science, or a related field

Nice to have

  • Experience with enterprise requirement tools (e.g., Jama, Polarion, or DOORS)
  • Familiarity with ISO 13849 / IEC 61508 functional safety standards
  • Mentorship in Model-Based Systems Engineering (MBSE) methodologies

What the JD emphasized

  • functional safety standards
  • functional safety
  • Functional Safety (ISO 13849 / IEC 61508)
  • shipped products

Other signals

  • human-centered robotics
  • humanoid robot, Apollo
  • embodied AI
  • robotics stack
  • commercialization
  • mass production
  • perception suite
  • system-level architecture
  • requirements
  • performance
  • Autonomy software team
  • Hardware engineering teams
  • autonomy capabilities
  • system requirements
  • latency budgets
  • field-of-view mapping
  • resolution
  • compute allocation
  • complex trade studies
  • Interface Control Documents (ICDs)
  • Verification and Validation (V&V) strategy
  • functional safety standards
  • humanoid robot operating alongside humans
  • Visual-Inertial Odometry (VIO)
  • manipulation
  • obstacle avoidance
  • remote teleoperation
  • Stereo Vision vs. LiDAR
  • edge-compute vs. centralized processing
  • global vs. rolling shutter
  • photon-to-action latency
  • compute utilization (TOPS)
  • memory bandwidth
  • spatial Field of View (FOV) coverage
  • requirements traceability
  • Jama, Polarion, or DOORS
  • product-level goals
  • hardware and software specifications
  • perception sensors
  • compute cluster
  • autonomy software stack
  • multi-disciplinary teams
  • master Verification and Validation plan
  • perception stack
  • KPIs
  • test methodologies
  • real-world, dynamic environments
  • Functional Safety (ISO 13849 / IEC 61508)
  • Hazard Analysis and Risk Assessments (HARA)
  • safety-critical detection zones
  • fail-safe degradation states
  • hardware redundancy requirements
  • Technical Adjudication
  • Autonomy team's desires
  • Hardware team's constraints
  • thermal limits
  • mechanical packaging
  • payload capacity
  • systems engineering rigor
  • Model-Based Systems Engineering (MBSE)
  • rigorous requirement writing
  • Perception Domain Expertise
  • modern perception modalities
  • Cameras, LiDAR, Radar, ToF, IMU
  • physics, error models, and integration challenges
  • Autonomy Familiarity
  • perception data is consumed by downstream algorithms
  • SLAM, object detection, kinematic planning, sensor fusion
  • Systems Rigor
  • shipped products
  • Analytical Skills
  • mathematical models in Python or MATLAB
  • simulate system-level latency, FOV coverage, or optical performance
  • early-stage architecture
  • Systems Engineering
  • Robotics
  • Electrical Engineering
  • Computer Science
  • electromechanical systems
  • autonomous vehicles
  • aerospace
  • advanced robotics