ML Engineer, Computer Vision AI - Flexible Hours

Incode; España; On-site; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Flexible working hours

Open vacation policy

Ownership & autonomy

Job summary

Incode seeks an experienced Machine Learning Engineer to design and deploy deep learning models powering biometric verification and document processing. You’ll build scalable ML pipelines, optimize for performance and cost, and collaborate with cross-functional teams to deliver production-ready solutions.

You will contribute to research and stay at the forefront of computer vision advancements, mentoring teammates and driving innovation in a fast-scaling unicorn environment.

Qualifications

• 5+ years in ML/Computer Vision with production experience.
• Strong Python programming and deep learning framework expertise.
• Experience deploying and optimizing models for performance and cost.

Responsibilities

• Design, build, and deploy deep learning models for identity verification tasks.
• Develop production-ready ML pipelines with scalability and cost efficiency.
• Collaborate with research, product, and engineering teams to integrate ML solutions.
• Drive data preprocessing, cleaning, and analysis to improve model performance.
• Mentor teammates and contribute to an innovation-driven culture.
• Publish research or contributions in relevant conferences or journals.

Skills

Machine Learning

Deep Learning

Computer Vision

Python

PyTorch

TensorFlow

Edge AI

End-to-end ML pipelines

Model deployment

Research contributions

Tools

PyTorch

TensorFlow

Job description

Incode seeks an experienced Machine Learning Engineer to design and deploy deep learning models powering biometric verification and document processing. You’ll build scalable ML pipelines, optimize for performance and cost, and collaborate with cross-functional teams to deliver production-ready solutions.

You will contribute to research and stay at the forefront of computer vision advancements, mentoring teammates and driving innovation in a fast-scaling unicorn environment.