Staff ML Engineer

Responsibilities

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

Requirements

• 5+ years of machine learning experience with 4+ years specializing in face recognition, biometrics, face analysis, or computer vision from anywhere.
• Strong expertise in PyTorch and production-grade deep learning systems.
• Hands-on experience with face recognition frameworks such as ArcFace, FaceNet, InsightFace, RetinaFace, MTCNN, or similar technologies.
• Experience deploying large-scale ML models using TorchServe, Docker, Kubernetes, and AWS cloud infrastructure.
• Experience with MLflow or Weights & Biases (W& B) for experiment tracking and model lifecycle management in production environments.
• Strong Python programming skills with expertise in scalable software design and production ML architecture.
• Experience building highly available, low-latency inference systems for large-scale production environments globally.
• Strong understanding of distributed training, model optimization, and GPU acceleration.
• Proven experience leading technical architecture discussions, mentoring engineers, and driving engineering excellence in a team setting.
• Excellent problem-solving, communication, and stakeholder management skills.

Preferred Skills

• Experience with Vision Transformers (ViT), ResNet, MobileNet, YOLO, or similar computer vision architectures.
• Knowledge of TensorRT, ONNX Runtime, CUDA, or other model optimization frameworks for performance enhancement.
• Experience in fairness, bias mitigation, and responsible AI for biometric systems globally.
• Familiarity with SageMaker, Kubeflow, Triton Inference Server, or distributed ML platforms.