VP Robotics Engineering

we are looking for  15+ years in software engineering, with several years architecting integration or platform layers used in production at enterprise scale  Demonstrable working knowledge of at least 2 major robot OEM ecosystems at the SDK and API level: you have built against them, not read about them  Still keyboard-credible: you prototype, review PRs, and debug integrations. We will verify this in the interview process  Applied AI capability: production experience with agentic systems, tool use and function calling, and orchestration of long-running autonomous tasks  Fluency in the operational reality of robots in industrial settings: connectivity, safety interlocks, teleoperation and human-in-the-loop handover, degraded modes, and what actually breaks in deployment  Experience leading engineering teams and dealing with external partner engineering organisations  Clear written and spoken communication at executive level; comfortable presenting live demos  Technical depth expected  We hire for depth per area, not a keyword count. The named platforms and tools are examples; strong equivalents are fine. Expect to be tested on the areas below.  OEM platforms and SDKs: hands-on integration experience with platforms such as Boston Dynamics (Spot SDK), Agility Robotics, Unitree, ANYbotics, or comparable quadruped, humanoid, AMR, or drone ecosystems; understanding of their mission APIs, fleet managers, autonomy behaviours, and payload and data interfaces  Robotics middleware and interoperability: ROS 2 (topics, services, actions, DDS) at integration level; fleet interoperability standards such as VDA 5050, the MassRobotics Interoperability Standard, and Open-RMF; where these standards genuinely work and where they fall short  Agent-first integration: designing AI agents that plan, dispatch, and supervise robot missions; MCP servers and tool interfaces over robot APIs; orchestration frameworks such as LangGraph, Pydantic AI, or provider-native SDKs; permissioning, human-in-the-loop escalation, and audit trails for autonomous physical action  Robotics foundation models: working awareness of VLA and omni models for robots (for example SkildAI, Physical Intelligence, Nvidia GR00T) and what they change about the integration surface over the next 2 to 3 years  Industrial data and telemetry: MQTT, OPC UA, and event-driven ingestion of robot telemetry, inspection imagery, and sensor data into enterprise systems; time-series handling at fleet scale  Simulation and validation: using simulation environments (Isaac Sim, Gazebo, or OEM-native simulators) to validate integrations and demos without waiting on hardware availability  Enterprise integration: API design and consumption at scale, including REST, OData, GraphQL, and webhook/event patterns into ERP, EAM, and FSM systems; mapping robot missions onto work orders, assets, and service processes  Languages and platform: expert-level Python and TypeScript; able to read C++ where OEM SDKs demand it; production Kubernetes and containerisation, CI/CD, and edge deployment patterns for on-site connectivity  Security and safety posture: identity and authorisation for machine actors, network segmentation for OT environments, and the audit and traceability expectations that come with software commanding physical machines  What sets candidates apart  Prior work connecting robots or autonomous systems to business systems (ERP, EAM, WMS, MES) rather than to other robots  Exposure to asset-intensive or field service domains: utilities, manufacturing, aviation, energy, logistics  Experience in an OEM or fleet management vendor engineering organisation, seen from the inside  Standards body or interoperability consortium participation  0-to-1 experience standing up a new engineering capability inside a larger organisation  How we assess  A walkthrough of integrations you have personally architected and shipped against real robot platforms, a technical deep dive on your agent-first design approach and how you handle autonomy, safety handover, and auditability, and a working session on a live integration problem. References will be asked specifically about hands-on contribution and OEM engagement, not leadership style.  Practicalities  Location and working model: Virtual/Hybrid – location dependent  Compensation: Upon discussion  International travel to OEM partners, customers, and flagship events (Europe and US, some Asia)  IFS is a billion-dollar revenue company with 7000+ employees on all continents. Our leading AI technology is the backbone of our award-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting-edge. At IFS, we’re flexible, we’re innovative, and we’re focused not only on how we can engage with our customers but on how we can make a real change and have a worldwide impact. We help solve some of society’s greatest challenges, fostering a better future through our agility, collaboration, and trust. We celebrate diversity and understand our responsibility to reflect the diverse world we work in. We are committed to promoting an inclusive workforce that fully represents the many different cultures, backgrounds, and viewpoints of our customers, our partners, and our communities. As a truly international company serving people from around the globe, we realize that our success is tantamount to the respect we have for those different points of view. By joining our team, you will have the op