Description

You will advance large-scale AI systems that safeguard platform integrity and user trust by building our Safety Foundation Model. You'll leverage state-of-the-art multimodal models to create intelligent tooling for curating training and evaluation data, enabling continuous improvement and proactive, real-time mitigation of safety risks. Your contributions will directly power production-grade moderation, detection, and enforcement systems.

Requirements

• 5+ years experience designing, building, and deploying large-scale ML systems in production.
• Graduate degree or equivalent in Computer Science, Engineering, or related field.
• Strong communication and collaborative problem-solving skills.
• Hands-on experience fine-tuning VLMs, LLMs, or large multimodal models is a plus.
• Experience with data quality, evaluation, synthetic data, or ML infrastructure in safety systems is a plus.

Responsibilities

• Design, build, and own core components of Safety AI Systems for large-scale data generation, labeling, and evaluation.
• Fine-tune open-weight multimodal LLMs and deploy them as agents for synthetic data generation and automated labeling.
• Build robust pipelines for curating and evaluating multimodal training data, including human-in-the-loop systems.
• Develop benchmarks and evaluation frameworks to measure model behavior, data quality, and safety impact.
• Architect systems for continuous learning with weekly gated rollouts and feedback loops from real-world failures.