Senior Computer Vision & Edge AI Engineer

Senior engineer with full end-to-end technical ownership of the vision core of an industrial visual inspection product built on the NVIDIA platform. Combines deep expertise in unsupervised anomaly detection and defect segmentation with production-grade GPU inference optimization and complete model lifecycle management. Also able to design and lead the evolution of the pipeline orchestration toward a high-performance native C core delivering real-time decisions on the factory floor.

Qualifications :

Deep Learning & Computer Vision

• Expert-level PyTorch: CNN / transformer vision architectures training evaluation and rigorous ONNX export (zero train/serve skew).
• One-class / unsupervised anomaly detection: PatchCore EfficientAD PaDiM studentteacher normalizing flows and their real failure modes (reference-set contamination threshold calibration with few or no defective samples synthetic defects via cut-paste / DRAEM over-rejection drift).
• Supervised defect segmentation and detection (encoderdecoder DETR-family): training acceptance criteria and imbalanced datasets.
• Methodological rigor: AUROC / AUPRO alongside plant-level metrics (escape rate false-reject rate) and regression validation against recorded data.

C & Real-Time Systems

• Expert-level modern C (C17/20) for real-time vision pipelines in addition to expert Python.
• High-performance systems design: native pipeline/orchestrator coordinating capture pre-processing inference and post-processing while keeping data in memory and avoiding unnecessary copies and hops.
• Hard latency budgets: determinism watchdogs and graceful degradation.
• Linux Docker Git and CI as the natural working environment.

GPU & Inference Optimization (NVIDIA)

• Solid CUDA: execution model streams CUDA Graphs memory management (pinned unified pre-allocation) writing and debugging custom kernels.
• GPU libraries: cuBLAS NPP CV-CUDA Thrust or equivalents for accelerated image pre-processing and scoring.
• TensorRT in production: engine building mixed FP16 / INT8 precision with custom quantization calibration precision-degradation diagnosis and dynamic batching.
• Triton Inference Server in production.
• Profiling with Nsight Systems / Nsight Compute; p99 latency characterization per stage and finding the real bottleneck before optimizing.

Data & MLOps

• Model versioning and registry (MLflow or equivalent) reproducibility and dataset curation (CVAT).
• Traceability: able to demonstrate which model data and version produced a given result.
• Models in production: monitoring drift detection and a retraining / rollback policy.

Nice to Have

• Custom TensorRT plugins.
• Industrial cameras GigE Vision (ideally Basler pylon); optics lighting and photometric calibration (flat-field).
• Anomalib (advanced use or upstream contributions).
• Manufacturing / quality context (automotive or another regulated industry); ISA-95 and IEC 62443.
• Public cloud and cloud MLOps (ideally Azure: IoT Edge ML).
• Publications talks or open source in vision / anomaly detection.

Información adicional :

Qué podemos ofrecerte:

• Formarás parte de un gran equipo de profesionales con inquietud y motivación por el desarrollo y la programación y participaras en nuevos y punteros proyectos para la compañia.
• Trabajarás a jornada completa con un horario flexible bajo un modelo de trabajo 100% remoto.
• 22 días de vacaciones  2 de asuntos propios.
• Formación por parte de la empresa para que puedas seguir desarrollándote y promocionar dentro del plan de carrera que existe para ti.
• Podrás asistir a eventos y conferencias relevantes del sector.
• Contrato indefinido.
• Estabilidad y buen clima laboral.
• Salario Competitivo.
• Acceso a ventajas del grupo de empresa
• Retribución flexible y más beneficios

Remote Work :

No

Employment Type :

Full-time