Responsibilities
Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition)
Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX
Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning, curate balanced training sets and generate synthetic data to address quality and diversity gaps.
Production Engineering: Own the path to production. Optimize models for low‑latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS.
Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.
Qualifications
Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis.
Deep expertise in computer vision and biometrics, especially face recognition.
Fairness & Ethics: Understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact.
Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). Write clean, modular, production‑ready code.
Systems Architecture: Experience designing end‑to‑end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow.
Cloud Native: Hands‑on experience scaling training jobs on multi‑GPU clusters and deploying services on AWS (SageMaker, EC2, EKS).
Nice to Have
Research Publications: Papers in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
Large Scale Search: Experience with vector databases (e.g., Milvus, Faiss) and approximate nearest neighbor (ANN) search algorithms.
Familiarity with privacy, security, and compliance in biometric systems.
Mobile/Edge Experience: Experience porting models to edge or mobile devices utilizing frameworks such as CoreML, LiteRT, and/or TFLite.
Synthetic Data: Experience using GANs or diffusion models to generate synthetic faces for training.
Strong communication skills.
Equal Opportunities Jumio is a collaboration of people with different ideas, strengths, interests and cultures. We welcome applications and colleagues from all backgrounds and of all statuses.
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Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition)
Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
Architect, train, and optimize models using PyTorch, Tensorflow, and/or JAX
Own and evolve end-to-end ML pipelines, from data ingestion to deployment. Design automated pipelines (Airflow) for data ingestion and cleaning, curate balanced training sets and generate synthetic data to address quality and diversity gaps.
Production Engineering: Own the path to production. Optimize models for low‑latency inference (quantization, distillation, TensorRT/ONNX) and manage deployment on AWS.
Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.
Qualifications
Experience: 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis.
Deep expertise in computer vision and biometrics, especially face recognition.
Fairness & Ethics: Understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact.
Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). Write clean, modular, production‑ready code.
Systems Architecture: Experience designing end‑to‑end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow.
Cloud Native: Hands‑on experience scaling training jobs on multi‑GPU clusters and deploying services on AWS (SageMaker, EC2, EKS).
Nice to Have
Research Publications: Papers in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
Large Scale Search: Experience with vector databases (e.g., Milvus, Faiss) and approximate nearest neighbor (ANN) search algorithms.
Familiarity with privacy, security, and compliance in biometric systems.
Mobile/Edge Experience: Experience porting models to edge or mobile devices utilizing frameworks such as CoreML, LiteRT, and/or TFLite.
Synthetic Data: Experience using GANs or diffusion models to generate synthetic faces for training.
Strong communication skills.
Equal Opportunities Jumio is a collaboration of people with different ideas, strengths, interests and cultures. We welcome applications and colleagues from all backgrounds and of all statuses.
#J-18808-Ljbffr
