AI Scientist / ML Engineer

LUXUAV is an innovative technology company headquartered in Luxembourg, shaping next generation airspace security for defence, civil, firefighting and police missions. We build a fully integrated Unmanned Aerial Vehicles (UAVs) ecosystem that connects mission systems across ground, air, and stratosphere into one coherent governmental and commercial architecture.

As AI Scientist / ML Engineer, you will own LUXUAV's sovereign AI capability: conduct original ML research, train and optimize proprietary perception models on classified aerial datasets, and build the end-to-end infrastructure that productionises them. This role is split between applied research (novel model design, domain-specific learning methods, adversarial robustness) and MLOps (training pipelines, model CI/CD, edge deployment automation).

Key Responsibilities

AI Research & Model Development

• Conduct original research into perception architectures tailored to aerial defense use cases: small object detection at variable altitude, multi-class threat classification, camouflage/occlusion robustness, and EO/IR domain adaptation.
• Design, train, and evaluate real-time detection models (YOLOv8+, RT-DETR, custom architectures) and propose novel adaptations where COTS solutions fall short of operational requirements.
• Research and implement domain adaptation and transfer learning strategies to maximize model performance on limited, classified aerial datasets without external data augmentation from third-party sources.
• Develop active learning and semi-supervised learning pipelines to minimize annotation cost on high-security datasets.
• Investigate and prototype multi-task learning approaches combining detection, segmentation, and scene understanding in a single inference pass for onboard efficiency.
• Develop and maintain multi-target tracking modules (ByteTrack, StrongSORT, or custom) as standalone, testable components delivered to the CV Engineer for pipeline integration.
• Maintain awareness of state-of-the-art ML research (NeurIPS, CVPR, ICCV, ECCV, ICML) and evaluate applicability to LUXUAV's operational context.

Edge Model Optimisation & Deployment Packaging

• Optimize trained models for onboard GPU hardware (NVIDIA Jetson, Qualcomm RB5) via quantization (INT8/FP16), structured pruning, and knowledge distillation.
• Export and validate optimized models through TensorRT, ONNX Runtime, and OpenVINO toolchains; produce deployment packages with performance benchmarks (latency, throughput, memory footprint) for handoff to the CV Engineer.
• Define and maintain model performance contracts (accuracy floors, latency ceilings), gating production promotion.

ML Infrastructure & MLOps

• Design and maintain GPU training pipelines (multi-GPU, distributed training) on LUXUAV's on-premise GPU cluster.
• Own experiment tracking, model registry, and reproducibility tooling (MLflow, DVC, or equivalent); enforce dataset and model versioning across all AI projects.
• Build and operate CI/CD pipelines for model releases: automated regression testing, benchmark gating, staged rollout, and rollback procedures.
• Manage the on-premise multi-GPU server cluster: resource scheduling, utilization monitoring,
• driver/framework version management, and capacity planning.

Synthetic Data & Digital Twin Integration

• Design and operate synthetic data generation pipelines (scene rendering, domain randomization, augmentation) to supplement classified real-world datasets, feeding LUXUAV's Digital Twin program.
• Maintain dataset quality pipelines: annotation validation, class balance monitoring, and distribution drift detection between training and operational data.

Security & Data Governance

• Enforce air-gapped data handling protocols for all classified training datasets: access control, lineage tracking, and audit logging.
• Ensure all ML tooling, model artifacts, and training infrastructure comply with data classification policies and export control requirements.

Experience & Skills

Required Qualifications & Experience:

• Master's or PhD in Machine Learning, Computer Vision, Applied Mathematics, or related field.
• 5+ years of hands-on experience in ML research and model development for real-world vision tasks (detection, classification, segmentation, tracking).
• Demonstrated ability to go from research idea to working implementation: paper reproduction, ablation studies, and production-grade code.
• Strong proficiency in PyTorch; familiarity with TensorFlow/Keras and ONNX.
• Proven experience with detection/segmentation frameworks (YOLOv8+, RT-DETR, Detectron2,
• MMDetection).
• Practical expertise in model optimization for edge: quantization, pruning, distillation, TensorRT/ONNX Runtime export.
• Experience with domain adaptation, transfer learning, and data-efficient learning techniques.
• Hands-on experience building MLOps pipelines: experiment tracking (MLflow, Neptune.ai, W&B), dataset versioning (DVC), and model CI/CD.
• Proficiency in Python (NumPy, SciPy, scikit-learn, Pandas) and pipeline automation scripting.
• Experience managing or operating multi-GPU Linux training environments (CUDA, cuDNN, SLURM or equivalent).
• English: Upper Intermediate or higher.
• Good communication skills and ability to cooperate with adjacent engineering teams.
• Free criminal record

Preferred Qualifications & Experience:

• Track record of publication at NeurIPS, CVPR, ICCV, ECCV, ICML, IROS, or equivalent venues; open-source contributions to ML or CV frameworks.
• PhD is strongly preferred for the research component.
• Experience with active learning, semi-supervised learning, or self-supervised pretraining on domain-specific datasets.
• Familiarity with aerial imagery specifics: overhead perspective, small object detection, multi-scale challenges, EO/IR domain adaptation.
• Experience with synthetic data generation and domain randomization (Blender, NVIDIA Isaac Sim, Unreal Engine/AirSim, or equivalent).
• Experience with adversarial robustness evaluation and model hardening for safety-critical deployments.
• Experience with distributed training frameworks (PyTorch DDP, DeepSpeed, FSDP).
• Familiarity with on-premise MLOps stacks (self-hosted MLflow, MinIO, Prometheus/Grafana for GPU monitoring).
• Familiarity with DO-178C and MISRA C/C++ as they apply to ML model validation in airborne systems.
• National from a NATO member country or one of the following NATO Indo-Pacific partners: Australia, Japan, South Korea, New Zealand or Ukraine.