DevJobs

Head of Algorithm

Overview
Skills
  • Deep learning Deep learning ꞏ 10y
  • Microservices Microservices
  • CI/CD CI/CD
  • Azure ML Azure ML
  • Docker Docker
  • Kubernetes Kubernetes
  • Computer Vision ꞏ 10y
  • AUC Analysis
  • TensorRT
  • ROC
  • ONNX
  • Model Training
  • Model Optimization
  • Model Evaluation
  • Model Deployment
  • ML Lifecycle
  • GPU Inference Optimization
  • GPU Benchmarking
  • FRR Metrics
  • FRR
  • FP16
  • FAR
  • Data Labeling
  • Data Collection
  • CNN
  • Cloud ML
  • AUC
  • LLM Integration
  • Object Detection
  • Segmentation Models
  • Semi-supervised Methods
  • Unsupervised Methods

Founded in 2002, AU10TIX is the global leader in AI driven identity verification and management, protecting the world’s largest brands against advanced fraud. The company’s future-proof product portfolio helps businesses provide frictionless customer onboarding and verification in 4-8 seconds while staying ahead of emerging threats and evolving regulatory requirements.


We are Looking for Head of Algorithms for:

  • driving continuous improvement in detection rates across all algorithm domains (document fraud, deepfakes, biometrics) while ensuring production-grade performance, scalability, and reliability of deployed models
  • Providing technical direction, mentorship, and career development to the Algo group
  • Set the overall technical strategy and roadmap for all computer vision algorithm development
  • Drive data labeling strategy and ownership across the organization, coordinate with QC, Product and various teams to define intake processes and SLAs
  • Prepare and deliver technical presentations for diverse audiences: client-facing ML capability pitches, VP-level strategy decks, and internal architecture reviews
  • Ensure PII compliance in algorithm pipelines and participate in cross-departmental compliance mapping initiatives


Model Development & Research

  • Own and guide the design, training, and optimization of deep learning models for identity document classification, tampering detection, deepfake detection, and biometric analysis
  • Lead model architecture decisions and drive migration to modern architectures


ML Lifecycle & Infrastructure

  • Own the end-to-end ML pipeline: data gathering, labeling strategy, training, evaluation, versioning, and deployment
  • Drive cloud migration of training pipelines to Cloud ML (compute clusters, experiment tracking, model registry, CI/CD integration)
  • Oversee inference optimization: ONNX export, TensorRT FP16 acceleration, GPU benchmarking, and microservices packaging
  • Define and maintain evaluation frameworks including demographic fairness testing, ROC/AUC analysis, FAR/FRR metrics, and detection rate tracking at fixed false-alarm thresholds


Requirements:


  • 10 years of hands-on experience in deep learning and computer vision, with at least 5 years in a senior leadership role managing team leads.
  • Proven experience leading and scaling technical teams in a director-level or equivalent capacity
  • Strong expertise in CNN architectures and computer vision pipelines
  • Production experience with the full ML lifecycle: data collection, labeling, training, evaluation, optimization, and deployment
  • Solid understanding of GPU inference optimization and benchmarking
  • Strong communication skills, ability to present complex ML topics to both technical and non-technical audiences


Nice to Have


  • Domain experience in identity verification, document analysis, or fraud detection
  • Experience with deepfake detection (document-level and biometric)
  • Experience with cloud ML platforms (Azure ML preferred: compute clusters, experiment tracking, model registry)
  • Familiarity with unsupervised/semi-supervised methods
  • Knowledge of microservices architecture patterns and containerized deployment (Docker, Kubernetes)
  • Experience with object detection frameworks and segmentation models
  • Background in LLM integration for document extraction tasks

AU10TIX