Capability Engineer Gen Pet Center Technologies

Job Description:

The Capability Engineer designs, builds and scales the next-generation technology backbone of Royal Canin’s R&D Pet Centers - cameras and smart sensors, edge devices, local infrastructure of GPU servers and on-premise machine-learning models - so that every meal and every protocol generates reliable, decision-grade behavioural and intake data. Based in Lewisburg (RLE), the role pilots and proves these technologies at RLE, then leads their standardised scale-up to Aimargues (RCA) and, over time, to the wider Mars Pet Center network.

It takes AI-assisted observation (palatability, digestibility, welfare and food-safety/quality signals) from prototype to secure, cross-site operation, releasing associates from manual scoring and feeding harmonised datasets into Monarch and then feed Royal Canin’s multi-modal predictive models. Working with Pet Center operations at both sites, R&D, Digital/IT, MGS Services, MGS Labs and external partners, the engineer makes RLE the technology pilot site and ensures that what works there is replicated at RCA, accelerating learning cycles and speed to market.

Bachelor’s or Master’s degree in computer or electrical engineering, computer science, data science, mechatronics or a related discipline, or equivalent practical experience. Training or certification in networking, server/infrastructure, edge or cloud computing, cybersecurity, AI/machine learning or project management is preferred.

• Hands-on experience designing and deploying camera/video systems and IoT sensors in operational environments (IP cameras, PoE networks, video management, time synchronisation, lighting and placement).

• Practical experience putting computer-vision/machine-learning models into production:
  Python, a deep-learning framework (e.g. PyTorch), containerisation (e.g. Docker), model optimisation for GPU/edge (e.g. ONNX, TensorRT) and validation using precision/recall-type metrics; familiarity with edge devices (e.g. NVIDIA Jetson, Raspberry Pi) is an advantage.

• Experience verifying and monitoring ML models in production: validation on held-out subjects and continuous data, drift detection, model and dataset versioning, retraining workflows and documentation of model performance over time.

• Rapid prototyping mindset: builds working prototypes quickly, including with AI-assisted coding tools, and turns successful ones into maintainable, documented, scalable solutions; experience integrating or customising ML tooling (annotation, training, experiment tracking) is an advantage.

• Working knowledge of server, storage and network infrastructure (Linux/Windows, GPU servers, virtualisation, backup, monitoring, TCP/IP, identity and access, firewalls and isolated/air-gapped designs) and cybersecurity fundamentals.

• Data engineering skills: data pipelines, metadata models, data-quality checks and integration with data lakes or research data platforms.

• Experience taking technology from pilot to multi-site scale: sizing, business cases, standardisation, vendor management, documentation, change management and user training; comfortable working across sites, cultures and time zones (US/Europe).

• Strong team player in cross-functional project teams: translates between scientists, animal-care operators, facilities and IT; builds trust with end users, troubleshoots complex issues, analyses root causes and communicates clearly with technical and non-technical stakeholders.

• Experience in an R&D, laboratory, animal-care, manufacturing or similarly regulated environment is preferred; exposure to animal-behaviour data or annotation/labelling tools is an advantage.

• Own the cross-site Next-Gen Pet Center effectiveness technology roadmap: transla…