Tech Lead Machine Learning Engineer - GenAI Post Train, Monetization Generative AI

heights, this is the team for you.

As a Machine Learning Engineer, you will drive innovations in post-training optimization, reinforcement learning, and fine-tuning techniques to maximize the performance of generative AI models. You will work on multi-modal diffusion models, transformer architectures, and various RL algorithms to adapt pre-trained models into highly performant, domain-specific AI solutions.

Responsibilities

1. Develop and implement fine-tuning strategies for large-scale diffusion models (T2V, I2V, T2I) to achieve SOTA performance in advertising and creative applications.
2. Optimize reinforcement learning methods (e.g., DPO, PPO, GRPO) to refine generative model outputs, ensuring alignment with human preferences and business objectives.
3. Enhance model personalization by integrating domain adaptation, contrastive learning, and retrieval-augmented generation techniques.
4. Work closely with pre-training teams to refine and extend model capabilities, ensuring seamless adaptation from foundational training to specialized, high-precision use cases.
5. Collaborate with application teams to deploy fine-tuned models into real-world content generation pipelines, optimizing for latency, efficiency, and content quality.
6. Advance model evaluation and signal growth strategies, designing innovative objective and subjective evaluation metrics for continuous model improvement.
7. Integrate novel training methodologies, such as self-supervised learning, active learning, and reinforcement learning-based data curation, to enhance generative model quality.
8. Explore cutting-edge techniques from academia and open-source communities, driving innovation in generative AI and maintaining TikTok's leadership in the field.

Qualifications

Minimum Qualifications:

1. B.S., M.S., or Ph.D. in Computer Science, Electrical Engineering, or a related field. 5+ years of industry experience in machine learning, deep learning, and large-scale AI model optimization. Expertise in PyTorch, diffusion models, and transformer architectures.
2. Strong background in fine-tuning large models for vertical applications in multi GPU settings. Hands-on experience with reinforcement learning (DPO, PPO, GRPO), contrastive learning, and retrieval-based methods. Deep understanding of generative model evaluation, multi-modal learning, and domain adaptation techniques.
3. Experience in scaling model fine-tuning and inference on large GPU clusters. Strong proficiency in model distillation, quantization, and memory-efficient optimization techniques (e.g., LoRA, QLoRA, ZeRO, DeepSpeed). Familiarity with distributed computing frameworks (Ray, Triton, vLLM) for large-scale AI training.
4. Ability to design iterative data curation loops that enhance model learning signals and domain relevance. Experience in active learning, dataset distillation, and self-improving model pipelines.

Job Information

[For Pay Transparency] Compensation Description (annually)

The base salary range for this position in the selected city is $208800 - $438000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.