Applied Researcher 4, AI Safety & Multimodal

What you'll do

• Build AI foundation models through all phases of development including design, training, evaluation, and implementation.
• Leverage Pytorch, AWS Ultraclusters, and Huggingface to extract insights from large volumes of numeric and textual data.
• Conduct high-impact applied research to integrate the latest AI developments into next-generation customer experiences.
• Develop and deploy safety guardrails for multimodal systems including VLMs, MLLMs, and MM-RAG architectures.
• Perform red teaming and vulnerability assessments for multimodal applications using image, video, and text modalities.
• Translate complex technical research into tangible business goals for product and technology leaders.
• Author internal technical reports or conference submissions summarizing novel methods and research findings.
• Own and pursue a research agenda by identifying impactful problems and autonomously carrying out long-running projects.

What we're looking for

• Must have a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field.
• Must have an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field plus 2 years of experience in Applied Research.
• Must have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• Must have a deep understanding of the foundations of AI methodologies.
• Must have experience building large deep learning models for language, images, events, or graphs.
• Must have expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Must have experience with red teaming and deploying safety guardrails for multimodal systems, including VLMs, MLLMs, and MM-RAG architectures.
• Must have an engineering mindset with a track record of delivering models at scale for training data and inference volumes.
• Must have experience delivering libraries, platform-level code, or solution-level code to existing products.
• Must have a track record of producing high-quality machine learning ideas, such as first-author publications or projects.
• Must possess the ability to own a research agenda and autonomously carry out long-running projects.
• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, or Electrical Engineering (preferred).
• LLM PhD focus on NLP or Masters with 5 years of industrial NLP research experience (preferred).
• Multiple publications on pre-training large language models, SSL techniques, or model pre-training optimization (preferred).
• Member of a team that has trained a large language model from scratch with 10B+ parameters and 500B+ tokens (preferred).
• Publications in deep learning theory or at major conferences like ACL, NAACL, EMNLP, Neurips, ICML, or ICLR (preferred).
• PhD focused on optimizing training of very large deep learning models (preferred).
• Multiple years of experience or publications in model sparsification, quantization, training parallelism, or model compression (preferred).
• Deep knowledge of deep learning algorithmic and/or optimizer design (preferred).
• Experience with compiler design (preferred).
• PhD focused on guiding LLMs through supervised finetuning, instruction-tuning, or dialogue-finetuning (preferred).
• Demonstrated knowledge of transfer learning, model adaptation, and model guidance (preferred).
• Experience deploying a fine-tuned large language model (preferred).
• Demonstrated ability to reproduce and extend peer-reviewed AI research using modern open-source frameworks (preferred).
• Experience in AI Safety, LLM safety alignment, and guardrail development (preferred).
• Experience in safety and vulnerability assessment of multimodal applications using image/video and text (preferred).
• Experience designing controlled experiments and documenting reproducibility results for research (preferred).