Research Engineer Graduate (Monetization Technology - Business Integrity) - 2027 Start (PhD)

About the role

We are looking for strong machine learning engineers who are excited to grow their business understanding, build highly scalable machine learning models, and partner across disciplines with global teams to build a next-generation content understanding system for TikTok monetization. This role focuses on creating an ecosystem that rewards high-quality user experience and advertiser value, addressing challenges in intelligent moderation such as dynamic moderation rules, content complexity, sample scarcity, adversarial behaviors, and lack of interpretability.

Responsibilities

• Build a leading moderation system that enables end-to-end capabilities for accurate rejection decisions, interpretable reasoning, and intelligent remediation.
• Achieve fully automated moderation with performance surpassing human benchmarks.
• Collaborate with global teams to develop scalable machine learning models for content understanding.

Qualifications

Minimum Qualifications

• Completing or have recently completed a PhD degree in Computer Science, Software Engineering, Electronic Engineering, Automation, Mathematics, Statistics, or a related technical discipline (completed or expecting to graduate within 12 months).
• Hands-on experience implementing, training, or deploying models in one or more of the following areas: Ads, Search, Recommender Systems, NLP, CV, Multimodal, or Agent technologies through coursework, internships, or industry projects.
• Familiarity with the fundamentals of large language models (e.g., pre-training, fine-tuning, prompting, inference, and deployment), with practical experience in at least one of these stages.
• Strong software engineering fundamentals: clean, testable code, version control, and the ability to debug complex systems end-to-end.
• Proficient in Python and at least one deep learning framework such as PyTorch or TensorFlow.
• Strong problem-solving ability, a collaborative mindset, and a genuine interest in applying AI to real-world products.
• Must be able to commit to an onboarding date by the end of the year. Availability and graduation date must be stated clearly in your resume.

Preferred Qualifications

• Experience with large-scale distributed training (DeepSpeed, Megatron, FSDP) or high-throughput inference (vLLM, SGLang, TensorRT-LLM).
• Experience with production model deployment, serving optimization, or latency/cost tuning.
• Experience building data pipelines for large-scale training or evaluation.
• Experience building agentic systems, RAG pipelines, or multimodal applications.
• Familiarity with the ads, search, or recommendation industry stack.
• Open-source contributions, strong results in ML competitions (Kaggle, Tianchi, ACM/ICPC), or granted patents.

Pay

The base salary range for this position is $162,000 - $316,800 annually. Compensation may vary based on qualifications, skills, competencies, experience, and location. This role may also be eligible for additional discretionary bonuses, incentives, and restricted stock units.

Benefits

• Day one access to medical, dental, and vision insurance.
• 401(k) savings plan with company match.
• Paid parental leave.
• Short-term and long-term disability coverage.
• Life insurance.
• Wellbeing benefits.
• 10 paid holidays per year.
• 10 paid sick days per year.
• 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).