Biometric Vision Engineer - Face Recognition, EMEA

Jumio; United States; Remote; USD 170,000 - 250,000; Full time

Job summary

Jumio is seeking a senior machine learning engineer to lead biometric vision model development for face analysis and recognition, spanning data, fairness evaluation, and low-latency cloud inference.

You will design models, benchmark across datasets, mitigate bias, and own end-to-end ML pipelines, including data ingestion, training, and deployment, while mentoring engineers. Role level will be decided during interview.

Qualifications

• Multiple years of industry machine learning experience, including biometrics or face analysis.
• Deep computer vision expertise with face recognition.
• Experience identifying, measuring, and mitigating bias in CV.
• Expert Python skills with production-ready code.
• End-to-end ML pipelines and Airflow workflows.
• Experience scaling training on multi-GPU clusters and deploying on AWS.

Responsibilities

• Design biometric vision systems for face attributes, detection, and recognition.
• Benchmark models across datasets and operating conditions with fairness checks.
• Train and optimize models using PyTorch, TensorFlow, and/or JAX.
• Own data-to-deployment ML pipelines, including data ingestion and cleaning.
• Prepare models for production inference with quantization, distillation, TensorRT or ONNX, and manage cloud deployment.
• Mentor engineers and review code and designs across the team.

Skills

Python

Machine Learning

Computer Vision

Bias mitigation

Airflow

AWS

Multi-GPU training

Tools

PyTorch

TensorFlow

JAX

ONNX

TensorRT

Job description

Jumio is seeking a senior machine learning engineer to lead biometric vision model development for face analysis and recognition, spanning data, fairness evaluation, and low-latency cloud inference.

You will design models, benchmark across datasets, mitigate bias, and own end-to-end ML pipelines, including data ingestion, training, and deployment, while mentoring engineers. Role level will be decided during interview.