{"id":1293412,"url":"https://alion.io/job/grizmo-labs-staff-machine-learning-engineer","title":"Staff Machine Learning Engineer","company":{"id":3806474,"name":"Grizmo 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production-grade face recognition and biometric verification systems at enterprise scale.\n\n- 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.\n\n- Architect and own end-to-end ML pipelines covering data preparation, model training, experimentation, deployment, monitoring, and continuous model improvement.\n\n- Develop scalable model serving solutions using PyTorch, TorchServe, Docker, Kubernetes, and AWS cloud infrastructure.\n\n- Lead technical design discussions, define ML architecture, conduct design reviews, and mentor engineers on machine learning best practices.\n\n- Collaborate with AI researchers, platform engineers, and product teams to deliver secure, scalable, and production-ready computer vision solutions.\n\n- Drive continuous improvements in model performance, bias mitigation, system reliability, and production scalability.\n\nRequirements : \n\n- 5+ years of Machine Learning experience with 4+ years specialising in Face Recognition, Biometrics, Face Analysis, or Computer Vision.\n\n- Strong expertise in PyTorch and production-grade deep learning systems.\n\n- Hands-on experience with face recognition frameworks such as ArcFace, FaceNet, InsightFace, RetinaFace, MTCNN, or similar technologies.\n\n- Experience deploying large-scale ML models using TorchServe, Docker, Kubernetes, and AWS.\n\n- Experience with MLflow or Weights & Biases (W&B) for experiment tracking and model lifecycle management.\n\n- Strong Python programming skills with expertise in scalable software design and production ML architecture.\n\n- Experience building highly available, low-latency inference systems for large-scale production environments.\n\n- Strong understanding of distributed training, model optimisation, and GPU acceleration.\n\n- Proven experience leading technical architecture discussions, mentoring engineers, and driving engineering excellence.\n\n- Excellent problem-solving, communication, and stakeholder management skills.\n\nPreferred Skills : \n\n- Experience with Vision Transformers (ViT), ResNet, MobileNet, YOLO, or similar computer vision architectures.\n\n- Knowledge of TensorRT, ONNX Runtime, CUDA, or other model optimisation frameworks.\n\n- Experience in fairness, bias mitigation, and responsible AI for biometric systems.\n\n- Familiarity with SageMaker, Kubeflow, Triton Inference Server, or distributed ML platforms\n\nSkills\nMachine Learning, Biometrics, MLOps, PyTorch, Tensorflow, AWS, Deep Learning, Python, Computer 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