Staff ML Engineer

Responsibilities:

• Design, develop, and deploy production-grade face recognition and biometric verification systems at enterprise scale within our global cloud infrastructure.
• Build, train, and optimise deep learning models for face detection, recognition, embedding generation, and biometric verification with a focus on high accuracy, fairness, robustness, and low-latency inference for international markets.
• Architect and own end-to-end ML pipelines covering data preparation, model training, experimentation, deployment, monitoring, and continuous model improvement using AWS cloud infrastructure globally.
• Develop scalable model serving solutions using PyTorch, TorchServe, Docker, Kubernetes, and AWS cloud services to support our worldwide operations.
• Lead technical design discussions, define ML architecture for global deployments, conduct design reviews, and mentor engineers on machine learning best practices across all regions.
• Collaborate with AI researchers, platform engineers, and product teams to deliver secure, scalable, and production-ready computer vision solutions for our expanding international client base.
• Drive continuous improvements in model performance, bias mitigation, system reliability, and production scalability for enterprise-scale applications worldwide.

Requirements:

• 5+ years of Machine Learning experience with 4+ years specialising in Face Recognition, Biometrics, Face Analysis, or Computer Vision at an international level.
• Strong expertise in PyTorch and production-grade deep learning systems for global deployment.
• Hands-on experience with face recognition frameworks such as ArcFace, FaceNet, InsightFace, RetinaFace, MTCNN, or similar technologies used in cross-border identity verification.
• Experience deploying large-scale ML models using TorchServe, Docker, Kubernetes, and AWS to support our global footprint including Europe, Asia Pacific, and the Americas.
• Experience with MLflow or Weights & Biases (W& B) for experiment tracking and model lifecycle management in distributed environments.
• Strong Python programming skills with expertise in scalable software design and production ML architecture suitable for international teams.
• Experience building highly available, low-latency inference systems for large-scale production environments serving a global user base.
• Strong understanding of distributed training, model optimisation, and GPU acceleration to support our worldwide infrastructure.
• Proven experience leading technical architecture discussions, mentoring engineers, and driving engineering excellence across diverse cultural teams.
• Excellent problem-solving, communication, and stakeholder management skills for a global technology company.

Preferred Skills:

• Experience with Vision Transformers (ViT), ResNet, MobileNet, YOLO, or similar computer vision architectures used in international markets.
• Knowledge of TensorRT, ONNX Runtime, CUDA, or other model optimisation frameworks for global cloud environments.
• Experience in fairness, bias mitigation, and responsible AI for biometric systems to ensure compliance across different jurisdictions.
• Familiarity with SageMaker, Kubeflow, Triton Inference Server, or distributed ML platforms used by multinational corporations.