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Principal Applied Scientist
2 weeks ago
We are looking for a **Principal Applied Scientist** with expertise in areas like **Machine Learning**, **Large Language Models**, **Reinforcement Learning**, **Causal Inference** **to join our team at Microsoft Ads India. This role offers a unique opportunity to work on some of the most complex and high-impact challenges in online advertising, where your innovations will directly shape user experience, advertiser success, and the future of the digital advertising ecosystem.
At Microsoft Ads, you'll be solving web-scale optimization problems, from selecting relevant ads to modeling user interaction with ad content. The models and solutions you develop will impact **millions of users daily** and drive revenue growth for **Microsoft** ads** and the Microsoft Ads platform. This is a great opportunity for someone passionate about **artificial intelligence**, **optimization at scale**, and **transformative innovation**
**Responsibilities**:
- ** Drive AI innovation**: Lead the development of cutting-edge models that select and rank ads, predict user interaction, and optimize advertiser outcomes. You will leverage and advance Deep Learning, Reinforcement Learning, Causal Inference, and other techniques to solve complex problems.
- ** Optimize** at scale**: Design, build, and deploy models that operate at web scale, ensuring they are robust, scalable, and high performing in real-world settings. You will directly improve user engagement, ad relevance, and advertiser return on.
- ** Lead experimentation and impact**: Oversee large-scale online and offline experiments to continuously optimize and validate model performance, ensuring real-time impact on user and advertiser experiences.
- ** Collaborate and innovate**: Work closely with worldwide research, engineers, data scientists, and product teams to integrate your solutions into Microsoft Ads systems, driving cross-team collaboration and delivering impactful end-to-end solutions.
- ** Mentor and lead**: Provide strategic direction and technical mentorship to a team of researchers and engineers, fostering a culture **of innovation and continuous learning.**
**Qualifications**:
**Required**:
- 10+ years of experience in research or applied science roles with a focus on Deep Learning, Information Retrieval, Reinforcement Learning, Causal Inference, or related areas.
- Hands-on experience with large-scale models and frameworks such as PyTorch.
- Strong proficiency in programming languages like Python, C++, or C#.
- Publications in top-tier conferences like NeurIPS, ICML, CVPR, SIGIR, KDD, ACL, EMNLP, ICLR, WWW, WSDM or similar, demonstrating expertise in advancing the field.
- Bachelor’s degree in computer science, Statistics, or a related field.
**Preferred**:
- PhD in Machine Learning, AI, or related fields.
- Experience working with large language models / multi-billion parameter models, focusing on their efficient training and online inference.
- Background in developing or modifying deep learning algorithms/architectures to improve computational and memory efficiency.
- Experience in online advertising, search engines, or recommendation systems.
