Senior Associate, AI Engineer
PwC
CloudDockerKubernetesMicroservices
Senior AI Engineer – LLM-Based Content Moderation
TrustLab
full-time
Posted on:
Location: California • 🇺🇸 United States
Visit company websiteSalary
💰 $150,000 - $250,000 per year
Job Level
Senior
Tech Stack
AWSPython
About the role
- TrustLab deploys custom LLMs as part of its AI solution for Enterprise customers. We’re a fast-moving, venture-backed startup founded by senior leaders from Google, YouTube, TikTok, and Reddit, creating solutions for online safety, reputation and risk management.
- At TrustLab, your work won’t live in theory - it will power live systems used at large scale. You’ll develop, tune, and optimize LLM-driven solutions that interpret and reason about complex digital content, while experimenting rapidly from design to deployment and seeing immediate feedback from real-world use cases.
- Partnering closely with other engineers, researchers, and product leaders, you’ll pioneer new approaches to model training and evaluation, taking ownership from early R&D through to production launches, and ensuring your work directly shapes how millions of people experience AI-powered content.
- Train, evaluate, and monitor new and improved LLMs and other algorithmic models; Test and deploy content moderation models in production; Develop medium to long-term vision for content understanding-related R&D; Take ownership of results delivered to customers.
Requirements
- Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field. Ph.D. is a plus. Proficiency in Python. Experience with AWS and CI/CD processes & tools is a strong plus.
- Experience with prompt-engineering techniques and familiarity with multiple LLM providers.
- Several years of industry experience in NLP / Computer Vision, or making LLM’s work in production for non-trivial use cases, incl. strong familiarity with evaluation metrics for classification tasks and best practices for handling imbalanced datasets.
- Hands-on experience with debugging issues in production environments, especially on AWS.
- Impressive track record delivering results under time and resource pressure
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