Machine Learning Engineer Graduate (Trust and Safety Engineering) (Nov 2026)

FEU; Sydney; Hybrid; AUD 70,000 - 90,000; Full time

Job summary

TikTok is recruiting a Graduate Machine Learning Engineer to join the Trust & Safety team in Australia. You will work with experienced engineers to build scalable ML models, classifiers and data-driven tools to keep the platform safe and vibrant for users worldwide.

The role emphasizes collaboration, learning and applying ML, NLP and computer vision techniques from day one, with practical, real‑world projects and cross‑functional impact.

Qualifications

• Bachelor’s or Master’s degree in a technical field (CS/Software Eng).
• Solid knowledge in ML, pattern recognition, NLP, data mining or computer vision.
• Strong data structures and algorithms understanding.
• Excellent communication and teamwork skills.

Responsibilities

• Collaborate with world‑class engineers to build trust and safety systems for TikTok.
• Develop scalable classifiers, tools and models using ML, CV and data mining techniques.
• Iterate on models and improve trust and safety strategy.
• Work with cross‑functional teams to protect TikTok globally.

Skills

Strong communication

Teamwork

Problem solving

Analytical thinking

Education

Bachelor’s or Master’s degree in Computer Science/Software Engineering

Tools

Python

TensorFlow/PyTorch

Job description

Team Introduction

Building a world where people can safely discover, create and connect. The Trust & Safety (T&S) team at TikTok helps ensure that our global online community is safe and empowered to create and enjoy content across all of our applications. We have invested heavily in human and machine-based moderation to remove harmful content quickly and often before it reaches our general community.

We are looking for talented individuals to join our team in 2027. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at our Company.

Successful candidates must be able to commit to an onboarding date by end of year 2027. Please state your availability and graduation date clearly in your resume.

Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

As a Machine Learning Engineer, you'll have the chance to work with our clients and teams to address key business problems and identify areas of growth for the company. With your education and experience, you will be able to take on real-world challenges from day one.

Responsibilities

• Work with our world-class engineers to build industry-leading trust and safety systems for TikTok
• Develop and build up highly-scalable classifiers, tools, models and algorithms leveraging cutting-edge machine learning, computer vision and data mining technologies
• Improve our trust and safety strategy and work on model iterations
• Collaborate with cross-functional teams to protect TikTok globally

Qualifications

Minimum Qualifications:

• Individuals who are completing or have recently completed a Bachelor’s/Master’s degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
• Solid knowledge in at least one of the following areas: machine learning, pattern recognition, NLP, data mining, or computer vision
• Firm understanding of data structures and algorithms
• Great communication and teamwork skills

Preferred Qualifications:

• Passion about techniques and solving challenging problems
• Previous experience in applications of machine learning, pattern recognition, NLP, data mining, or computer vision
• Currently pursuing your PhD or Master degree in Computer Science or related engineering field.

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy.