About MitekMitek (NASDAQ: MITK) is a global leader in digital identity verification, biometric authentication, and fraud prevention. Our AI-powered platform combines computer vision, biometrics, and machine learning to help organizations securely onboard users and defend against emerging threats such as deepfakes and AI-driven fraud.Trusted by over 7,500 organizations worldwide, Mitek processes millions of identity transactions daily. Headquartered in San Diego, with a strong presence across Spain, France, Mexico, and the UK and the Netherlands. Visit us at
www.miteksystems.com. we are driven by a clear mission: protect what’s real.At Mitek, we believe that teams are more resilient, effective, and innovative when they benefit from a wide range of ideas, lived experiences, and perspectives.We know that a workforce reflecting the richness of our communities and customers helps us better serve their needs. These lived experiences influence our decisions, shape our products, services, and help us grow with intention. When it comes to talent, our goal is clear: to discover exceptional individuals and to ensure they discover us.We are a Virtual First organization that values flexibility, collaboration, and innovation, while fostering an inclusive environment where diverse perspectives drive better outcomes.Why Join This TeamWork on AI that fights AI-driven fraudDirect impact on protecting users from real-world threatsCollaborate with experts across fraud, biometrics, and AI researchOpportunities for growth across teams and domains within MitekA culture focused on collaboration, innovation, and continuous learningThe Impact You’ll MakeAs a Machine Learning Engineer, you will join our Fraud & AI Integrity group, specifically focused on deepfake detection, digital manipulation, and injection attack detection in selfie-based identity verification.This is a hands-on, production-focused role where you will design and deploy computer vision models that operate in adversarial environments. Your work will directly contribute to protecting users and businesses from sophisticated fraud attacks powered by AI.What You Will Do (Core Responsibilities):Design, train, and deploy machine learning models for image-based fraud detection, including: Deepfake detection; Injection attack detection; Digital manipulation analysis in biometric verificationWork end-to-end across the ML lifecycle: Dataset curation (large-scale, noisy, adversarial datasets); Model development and training; Evaluation and iteration using fraud-relevant metrics; Production deployment and monitoringBuild robust data pipelines, including: Data validation, cleaning, and labeling strategies; Handling class imbalance, bias, and distribution shiftDefine and execute evaluation frameworks focused on real-world performance: Precision/recall trade-offs; False positive vs. fraud detection balance; Robustness to unseen attack typesCollaborate closely with: Fraud & AI research teams; Data collection and annotation teams; MLOps and platform engineering; Product teamsContribute to production ML systems, ensuring: Scalability and reliability; Monitoring and performance tracking; Continuous improvement against evolving threatsWho You Are (Soft Skills):A pragmatic problem-solver who understands the gap between research and productionComfortable working in adversarial, fast-evolving problem spacesAble to clearly communicate technical concepts and trade-offsCollaborative and adaptable, with a strong sense of ownershipMotivated by building technology that has real-world impactWhat You Need (Required Knowledge, Technical Skills):Required Qualifications:Bachelor’s degree in Computer Science, Engineering, or related field2+ years of experience deploying machine learning models into productionStrong background in computer vision (image-based ML)Solid programming skills in PythonHands-on experience with PyTorch and/or TensorFlowExperience working with real-world datasets and building data pipelinesTech StacksCloud: AWSLanguages: PythonML Frameworks: PyTorch, TensorFlow, Scikit-learnData Tools: Pandas, OpenCVInfrastructure: Docker, CI/CD, cloud-based ML pipelinesWhat Would be Nice (Preferred Experience):Advanced degree (PhD or equivalent experience in Machine Learning or Computer Vision)Experience in fraud detection or adversarial ML domainsExperience with deepfake detection, image forensics, or manipulation detectionFamiliarity with generative AI models (training or analysis)Background in data science or data engineeringWhat We Provide (Benefits):Competitive packageFull Remote contractAnnual LeaveHome Office AllowanceAnnual Bonus – up to 10%Health InsuranceLearning & Development: We promote continuous learning and support role-aligned development opportunities, with access to a complimentary LinkedIn Learning licence.
