Job Description

Job Description

Senior Machine Learning Engineer - Multimodal AI, Trust & Safety

Location: San Francisco, CA Bay Area or NY

*We are unable to sponsor at this time.⏰

📢New opening for a Senior ML Engineer to join a small but growing team within digital, gaming and streaming media. Opportunity to develop LLM across the organization and retrieval-augmented generation (RAG) frameworks for the greater good.

Summary:

• You will be part of a world-class ML/AI team, developing machine learning models for content moderation and classification. Working cross-functionally to turn business challenges into AI-driven solutions.
• Develop large-scale production ML models to solve online safety problems. Trust & Safety, content Intelligence, content moderation, fraud detection.
• Build and scale our retrieval-augmented generation (RAG) system that delivers analytic insights and automation to our product teams.
• Create and apply NLP, LLM AI models for customer safety in text, voice, audio and image classification.
• Analyze business use cases and identify areas where AI models may have predictive limitations or biases.
• Design, train and optimize neural networks, within frameworks; TensorFlow and PyTorch.

Experience:

• Minimum of 5-7 years of professional experience in machine learning, deep learning and NLP.
• Hands-on experience with generative AI models multi-modal AI
• Expertise in applying machine learning models in a professional environment with large, complex datasets.
• Expertise with Python, libraries pandas, scikit-learn, Jax, PyTorch and PySpark.
• Familiarity with multimodal AI models combining text, image, and video data.
• Passion for research publication, conferences (NeurlIPS, ICLR, ACL, ICML), AI research.

Education:

Master’s in Mathematics, Computer Science, Statistics, Engineering or similar degree (PhD preferred but not required)

Target Total Compensation: $300,000 - $425,000, including a fixed annual salary of $200,000 - $280,000, employee equity grant, and full employee benefits.

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