Robot Platform Engineer (human)

NEURA Robotics; München; Vor Ort; EUR 90.000 - 130.000; Vollzeit

Zusammenfassung

NEURA Robotics in Munich, Germany seeks a skilled robotics software engineer to advance its robot abstraction layer and middleware integration. You will map hardware capabilities to a shared platform API, design state machines for robust operation, and implement gRPC services for control and diagnostics.

Expertise in ROS 2, real-time patterns, and hardware interfacing is essential. The role emphasizes real-time control, middleware wiring, and end-to-end platform bring-up on real hardware, not

Qualifikationen

• Experience implementing robot abstraction layers mapping hardware capabilities to a common platform API.
• Proven ability to work with real-time control and low-latency middleware stacks.
• Hands-on exposure to bringing up real robot platforms end-to-end.
• Strong knowledge of ROS 2-based architectures and hardware interfaces.

Aufgaben

• Develop robot abstraction integration across hardware, joints, sensors, and end-effectors.
• Wire middleware graphs (ROS 2 Launch, DDS QoS, ROS 2 control YAML) and component pipelines.
• Design and implement operational state machines (BOOT to RECOVERY) with guards and timeouts.
• Implement gRPC-based server endpoints for mode commands, state queries, and diagnostics.
• Collaborate with Platform Architect and Robot Client SDK Engineer on API co-ownership.
• Contribute to integration test suites in simulation and on real hardware.

Kenntnisse

Strong C++

Real-time patterns

State machine design

gRPC / protobuf

Robot abstraction layer

ROS 2

Hardware integration intuition

DDS / QoS

Tools

ROS 2

OROCOS RTT

Apex.OS

MoveIt2 hardware interface

gRPC

protobuf

DDS QoS

Jobbeschreibung

Your mission & challenges

• Robot abstraction integration: working in robot abstraction layer code — implementing the abstraction surface that maps your platform's hardware capabilities (joints, limbs, end-effectors, sensors) to the shared platform API. Conceptually similar to authoring a ros2_control hardware interface, an OROCOS RTT component, or a vendor-SDK adapter — but more vertically integrated.

• Middleware wiring for your platform: typically ROS 2 launch graph + ros2_control YAML + DDS QoS on platforms using the standard middleware path; OROCOS RTT component pipelines on platforms with hard-RT control loops that exceed ROS 2 callback timing budgets; Apex.OS on SIL/PLd-certified configurations (Industrial MM). Sensor topic plumbing and ObsAssembler wiring regardless of middleware choice. Partnered with the Robotics Middleware Engineer for the broader middleware stack architecture.

• Operational state machine: BOOT → HOMING → OPERATIONAL → FAULT → E-STOP → RECOVERY for your specific platform, including all transition guards, entry/exit actions, and timeout handling.

• Mode switching: active controller selection, mode transition guards, safe mode entry/exit, gravity-compensation-only mode.

• Power-on/off sequences: boot sequencing, drive enable/disable ordering, homing procedure orchestration, controlled shutdown.

• Watchdog and fault management: fault classification (recoverable vs safety-critical), recovery action selection, escalation to E-stop.

• Robot-level health monitoring: aggregating diagnostics, joint state health, drive temperature, joint limit proximity, communication latency.

• gRPC server implementation: mode commands, state queries, fault acknowledgement, streaming joint state, operational state, diagnostics.

• Proto message co-ownership for the robot control API with the Platform Architect and Robot Client SDK Engineer.

• Platform integration test suite: end-to-end CI test coverage in simulation (MuJoCo / Isaac Sim) and on real hardware.

What we can look forward to

• Strong C++ — modern features (C++17 or later), real-time-safe patterns, allocation discipline.

• State machine design experience for complex robotic systems: clean separation of states, transition guards, fault handling. Familiarity with hierarchical state machine patterns (Boost.SML, sc::statechart, BehaviorTree.CPP, or equivalent).

• gRPC and protobuf: API server implementation, streaming RPCs, backward-compatible proto evolution.

• Robot abstraction layer experience from prior production work — expressed through ANY ONE of the following equivalent paths (no single framework is a hard gate): ros2_control hardware interfaces (Jazzy or Humble); MoveIt2 hardware interface authoring; OROCOS RTT components for hard-RT control pipelines; Apex.OS deployment on a real platform; vendor SDKs such as Franka Control Interface, Universal Robots URX, KUKA Sunrise.OS, ABB EGM, or comparable; or proprietary in-house robot abstraction layers from prior production work.

• Experience bringing up a real robot platform end-to-end — not only simulation. Hardware integration intuition: timing assumptions, sensor calibration discipline, fault scenario design.

Nice to Have

• Hands-on with MORE THAN ONE of the robot abstraction layer paths above — multi-stack experience speeds onboarding.

• ROS 2 (Jazzy or Humble) production hands-on — currently the most common middleware path on NEURA platforms.

• OROCOS RTT exposure where the platform's hard-RT requirements call for it.

• Apex.OS familiarity for the Industrial MM SIL-certified configuration.

• Cluster-specific depth — for the Mobile Manipulator anchor: Nav2 or equivalent AMR navigation stack, docking cycles, optional PROFINET familiarity; for the Legged anchor: floating-base state estimation concepts, gait controller integration, fall detection patterns; for the Cobot anchor: ISO 10218 / ISO/TS 15066 collaborative-robot mode design.

• Multi-platform robot integration experience — having shipped state machine logic for two or more distinct robot platforms.

• Fault tree analysis (FTA) or FMEA for robotic state machine design.

• gRPC-Gateway or REST/gRPC bridging for customer-facing API surfaces.

• Note on internal stack familiarity: direct experience with NEURA's internal RAL is not required. The codebase is the entry point of the role, not the prerequisite. Candidates with comparable robot abstraction layer experience from prior production work onboard quickly through the existing patterns, regardless of which middleware path that experience came through.