Senior Machine Learning Engineer (MLOPs)

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

The AI & Biometrics team is building a state-of-the-art iris recognition engine that works on the 1B+ people scale. In order to do this, we use a fusion of custom optics, hardware, and on-device machine learning, combined with large-scale data collection in more than 20 countries to amass over several million images monthly. These images need to be pre-processed and passed through both external and in-house labelling services.

About the Opportunity:

You will be leading the development of internal-facing tools that will enable the development of cutting-edge computer vision models, making you a key to our success in onboarding a billion people in crypto.

As the AI team scales and the number of deployed models grows, you will industrialize our ML frameworks and implement MLOps tools to take us to the next level.

In this role you will:

• Architect, implement, test, and maintain core technology of our deep learning training and inference pipelines.
• Create frameworks and build tools for the entire ML lifecycle: Training, inference, deployment, MLOps, active learning, etc. 
• Work transversely with the whole AI team to define their needs and implement the right tool
• Produce clean, maintainable and production-proof code and frameworks

About You:

• You have 5+ years of experience using Python and a ML framework (preferably Pytorch) building robust and clean ML tools (e.g. training and inference frameworks)
• You have industry experience as a Software engineer or Machine Learning Engineer developing, debugging, testing, and deploying ML models. 
• You love clean and well-designed code, and want to raise the bar

Nice to have :

• Experience in Computer Vision, Active Learning
• Experience with biometric data
• Experience with MongoDB, AWS S3, Docker, Rust