Software Engineer, LLM - USDS

About the position You'll be an integral part of the USDS Cyber Defense & Engineering team, responsible for enhancing security tools and identifying vulnerabilities, with a specific focus on content assurance and the application of large language models (LLMs). You'll collaborate cross-functionally with partners inside and outside TikTok to fortify our products and users' security, helping to establish TikTok as the most trusted platform. We are seeking a versatile, forward-thinking, and outcome-driven Software Engineer to propel our projects forward. In this capacity, you will engage with diverse technical and non-technical teams across various regions, contributing to the development of innovative, AI-driven solutions to complex content moderation challenges. If you thrive in a dynamic environment and relish the opportunity to shape the strategic trajectory of a large global organization, this role offers an exciting prospect. The ideal candidate will possess demonstrated problem-solving abilities, sound business acumen, and a track record of collaborating with multiple teams to successfully deliver projects. They should exhibit a genuine passion for safeguarding the security and privacy of our users, and a strong understanding of how to leverage cutting-edge technology, like LLMs, to achieve that goal.

Responsibilities

• Collaborate Across Teams: Work closely with data scientists, software engineers, machine learning engineers, and product managers to understand the recommendation engine.
• Deep Expertise in Recommender Systems: Leverage your expertise in machine learning and coding to gain an in-depth understanding of context-aware recommender systems.
• Understand Core System Components: Understanding of key modules in the recommender system, including recall, ranking, and reranking, ensuring high-quality, personalized recommendations at scale.
• End-to-End Ownership: In-depth understanding of the complete lifecycle of machine learning systems, from building and maintaining data pipelines and feature engineering, to training models and integrating them seamlessly into production environments.
• Ensure Security & Compliance: Work with cybersecurity teams to ensure that the recommender systems align with compliance standards and implement practices that enhance user trust and experience.
• Support Automation & Prototyping: Contribute to quick prototyping and proof-of-concept initiatives that automate rule reviews within the recommendation systems, ensuring both efficiency and compliance.
• Document & Ensure Accessibility: Build and maintain comprehensive documentation for data processes and machine learning models, ensuring transparency, accessibility, and consistency across teams.

Requirements

• Bachelor's degree or PHD. in Computer Science, Engineering, Mathematics, or a related field along with Experience in Recommendation Systems: Proven track record of designing, developing, and optimizing recommendation systems, particularly at scale.
• Machine Learning Expertise: Experience working with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, MXNet, or similar tools to build and deploy models.
• Hands-on experience in one or more of the following areas: Large Language Models (LLM), Machine Learning, Deep Learning, Recommender Systems, Data Mining, or Natural Language Processing
• Strong Programming Skills: Excellent programming skills, data structure and algorithm skills, proficient in C/C++ or Python programming language, candidates with awards in ACM/ICPC, NOI/IOI, Top Coder, Kaggle and other competitions are preferred.
• Solid Understanding of Algorithms: Deep knowledge of data structures, algorithms, and optimization techniques to solve complex technical challenges along with Problem-Solving Mindset: Excellent troubleshooting and debugging skills, with an ability to quickly address issues that arise in live environments.
• Collaboration & Communication: Strong teamwork and communication skills, with the ability to work effectively across interdisciplinary teams and clearly explain complex technical concepts.

Nice-to-haves

• Master's degree or PHD. in Computer Science, Engineering, Mathematics, or a related field along with Experience in Recommendation Systems: Proven track record of designing, developing, and optimizing recommendation systems, particularly at scale.
• Advanced Techniques: Experience with advanced recommendation algorithms such as matrix factorization, collaborative filtering, or deep learning-based methods.
• Production-Ready Systems: Hands-on experience in deploying machine learning models in production environments, with an understanding of scaling and performance tuning.
• Model Evaluation: Familiarity with model evaluation metrics (e.g., precision, recall, NDCG) and A/B testing to assess and improve system performance.
• Cloud & Containerization Expertise: Knowledge of containerization tools (Docker, Kubernetes) and microservices architecture to support scalable, distributed systems. Security Awareness: Understanding of security and compliance best practices for handling user data in machine learning applications.

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