Software Engineer, LLM - USDS

Tiktok-posted about 1 month ago

Full-time • Mid Level

Hybrid • San Jose, CA

5,001-10,000 employees

Broadcasting and Content Providers

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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.

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.

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.

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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