Staging environment
Free Lesson

How to Become a Machine Learning Engineer in 2026

30 min
Feb 16, 2026 5:00 PM
Virtual (Zoom)

In this video

What you'll learn

A realistic view of the machine learning roles in 2026

Understand how the role is evolving beyond model training into systems, deployment, and reliability.

The core skills ML engineers actually need

Learn which fundamentals in data, modeling, infrastructure, and software engineering matter most.

How ML engineers work with data, models, production systems

See how experiments become deployed, monitored ML systems.

Where to focus when learning or reskilling

Prioritize skills that translate to real-world impact.

How to move from experimentation to production ML

Understand what differentiates notebooks from production-ready ML systems.

Why this topic matters

The ML engineer role is often misunderstood as model tuning alone. In reality, it’s about building reliable systems around models. This session focuses on what the job actually looks like in 2026, helping learners invest in skills that matter long term.

You'll learn from

Aki Wijesundara, PhD

Aki Wijesundara, PhD

AI Founder | Google AI Accelerator Alum

Manu Jayawardana

Manu Jayawardana

AI Advisor | Co-Founder & CEO at Krybe | Co-Founder of Snapdrum

Previously at

Google
Meta
OpenAI
Amazon Web Services
NVIDIA
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