AI Engineer – LLM-Based Content Moderation

As an AI Engineer focused on LLM-Based Content Moderation in our Palo Alto startup, your key responsibilities include designing, developing, and optimizing AI models specifically for enhancing content moderation by improving precision and recall. You will fine-tune large language models (LLMs) for classification tasks, particularly in abuse detection, and employ advanced techniques like active learning and self-supervision to optimize model performance. Additionally, you’ll deploy and monitor these models in production, using real-world metrics to inform iterative improvements.

To qualify for the AI Engineer role focused on LLM-Based Content Moderation at our Palo Alto location, candidates should possess a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. The ideal candidate will have experience in AI/ML, particularly in natural language processing (NLP), deep learning, and proficiency with frameworks such as TensorFlow or PyTorch. Familiarity with evaluation metrics and techniques for handling imbalanced datasets is also essential, along with a demonstrated ability in fine-tuning transformer-based models like GPT or BERT.

The AI Engineer role is pivotal in developing sophisticated classifiers that enhance content moderation efficacy at our Palo Alto startup. By designing and tuning LLMs that excel at detecting abuse, you will be directly improving how content is understood and moderated online. Your work will not only involve crafting models but also optimizing their deployment to ensure they perform effectively in real-world settings, ultimately leading to a safer online experience for users across various platforms.

At our Palo Alto startup, an AI Engineer can expect a dynamic and collaborative work culture, where innovation and teamwork are highly valued. You will work alongside renowned industry leaders in AI and Online Safety, contributing to projects that make a real difference. The environment encourages both individual growth and teamwork, ensuring you have the support needed to excel in your role while having opportunities for professional development. The culture is inclusive and collaborative, where you can get to know your colleagues while focusing on your expertise.

AI Engineers in our Palo Alto office are presented with abundant growth opportunities, whether you wish to advance as a technical contributor or transition into management. The startup culture promotes learning and development, offering access to mentorship from industry veterans and exposing you to real-world applications of AI technology on large scales. By working on cutting-edge projects that directly impact online safety, you'll enhance your skills and be well-positioned for advancement within the company, making it a prime place for career growth.

When answering this question, focus on specific projects or examples where you have successfully fine-tuned LLMs. Discuss the frameworks you used, such as TensorFlow or PyTorch, and detail methods you employed, like supervised learning or reinforcement learning. Highlight any improvements resulting from your tuning efforts, such as increased accuracy or a reduction in bias.

In response to this question, outline your strategy for optimizing AI models, including techniques like active learning, domain adaptation, and self-supervision. Provide a specific example of a model you've optimized, the challenges you faced, and how your approach led to measurable performance improvements, emphasizing your analytical skills and attention to detail.

Discuss the strategies you apply to address imbalanced datasets, such as resampling techniques, using different evaluation metrics like F1-score, precision-recall, or applying domain adaptation methods. Offer examples from your past work where you successfully tackled this issue, emphasizing the importance of balanced data for model performance.

Explain your step-by-step approach to deploying AI models in production, including testing models in staging areas, setting up continuous integration/continuous deployment (CI/CD) pipelines, and monitoring the models post-deployment. Share any tools or practices you've used to ensure successful deployments and how you manage updates or changes to deployed models based on feedback.

Provide a clear example of a distinctive approach you proposed and the rationale behind it. Detail how you identified a problem or opportunity, the innovative idea you presented, and the outcomes that resulted. It's essential to demonstrate your creativity and ability to think critically in solving complex issues.

Discuss your methods for keeping up with the fast-changing field of AI, such as attending conferences, participating in webinars, reading scholarly articles, or engaging in online courses. Mention specific resources and communities that you contribute to or follow, showing your commitment to lifelong learning.

Describe your familiarity with relevant regulations, like GDPR or DSA, and how these frameworks influence model development and deployment. Share any situations in which you had to ensure compliance with such regulations, illustrating your understanding of the legal landscape surrounding AI in content moderation.

Prepare to discuss various AI techniques, like LLMs, that enhance content moderation accuracy and efficiency. Highlight specific benefits such as real-time moderation capabilities, reduced false positives, and the ability to scale moderation efforts to handle large volumes of content. Providing examples helps to solidify your argument.

Highlight a recent project that utilized NLP techniques, discussing the tools, methodologies, and outcomes. Explain the problem you were solving with NLP, the approach, and the results achieved, emphasizing contributions to content analysis or moderation that demonstrate your proficiency in this area.

Reflect on particular challenges you encountered in previous roles, such as data quality issues, model performance limitations, or integration with existing systems. Discuss the strategies you employed to overcome these hurdles, illustrating your problem-solving skills and resilience in the field of AI engineering.