Senior Applied ML Engineer – NLP & Deployment (Hybrid)

etechnology; Manila; Hybrid; PHP 104,000 - 142,000; Full time

Benefits offered by this job

Day 1 Employment

HMO & Life Insurance

Paid Annual Leaves

Retirement Package

Career Growth

Well-being Programs

Flexible Hybrid Schedule

Cross-Branch Collaboration

Job summary

Cambridge University Press & Assessment in Manila is seeking a Senior Applied Machine Learning Engineer to advance our NLP scoring and assessment platforms. The role blends research with production engineering, working in a hybrid setup two days in the office to deliver scalable ML solutions.

You will lead ML strategy, develop models in PyTorch or TensorFlow, oversee end-to-end lifecycle from data to deployment, and mentor engineers while communicating complex concepts to non-technical

Qualifications

• Proven experience delivering production-grade ML/NLP systems.
• Experience with PyTorch or TensorFlow and transformer models.
• Strong Python engineering across data prep, training, deployment, and monitoring.
• Ability to lead complex technical initiatives.

Responsibilities

• Define ML strategy and establish engineering standards for model architectures and validation.
• Design, train, and optimize NLP solutions to score candidate responses and lead R&D of future systems.
• Build robust experimentation frameworks assessing fairness, consistency and drift.
• Own end-to-end ML lifecycle from conception to cloud deployment and monitoring.
• Mentor engineers and translate complex concepts for non-technical stakeholders.

Skills

ML leadership

NLP expertise

Python development

Communication

Tools

PyTorch

TensorFlow

Job description

Cambridge University Press & Assessment in Manila is seeking a Senior Applied Machine Learning Engineer to advance our NLP scoring and assessment platforms. The role blends research with production engineering, working in a hybrid setup two days in the office to deliver scalable ML solutions.

You will lead ML strategy, develop models in PyTorch or TensorFlow, oversee end-to-end lifecycle from data to deployment, and mentor engineers while communicating complex concepts to non-technical