Lead Applied Scientist Security Models Training Team - Next-Gen frontier research

Overview

The Security Models Training team is expanding to drive the development of a new type of GenAI architecture that can effectively address the unique challenges of cybersecurity. This is a unique opportunity to engage in frontier research that is product-focused at the same time.

The Security Models Training team builds and operates the large‑scale AI training and adaptation engines that power Microsoft Security products, turning cutting‑edge research into reliable, production‑ready capabilities.

As Lead Applied Scientist, you will own end‑to‑end model development for security scenarios, set technical strategy across multiple model efforts and teams, including developing new model architectures, continual pre‑training, task‑focused fine‑tuning, reinforcement learning, and objective, benchmark‑driven evaluation.

You will drive training efficiency and reliability on distributed GPU systems, deepen model reasoning and tool‑use capabilities, and embed Responsible AI, privacy, and compliance into every stage of the workflow. The role is hands‑on and impact‑focused, partnering closely with engineering and product to translate innovations into shipped, measurable outcomes, defining quality gates and readiness criteria across teams, and mentoring senior scientists and engineers to scale results across globally distributed teams.

You will combine strong coding, experimentation, and debugging skills with a systems mindset to accelerate iteration cycles, improve throughput and cost‑effectiveness, and help shape the next generation of secure, trustworthy AI for our customers.

Responsibilities

You’ll work as part of an Applied Science team on high-impact, technically ambitious AI projects that directly shape the future of AI in Cyber security, with ownership for taking advanced research through to production impact.

• Technical Leadership & Ownership: set technical direction for major security domain initiatives and align roadmaps across multiple teams; lead security model programs spanning pre‑training, task tuning, reinforcement learning, and evaluation; translate cutting‑edge research into production‑ready capabilities. This role influences portfolio‑level technical tradeoffs, investment prioritization, and long‑term architecture decisions for security models.
• Advanced Model Design – Building and customizing deep learning model architectures (e.g., modifying transformer blocks, attention/memory modules, etc.) at the SLM/LLM scale; making principled architectural tradeoffs to improve reliability, robustness, and security‑specific behavior.
• Advanced Model Training – Apply deep expertise in pre-training, post-training, and reinforcement learning (RL) for both language and other modalities, including time-series. 
• Design & Evaluate Datasets – Build high-quality datasets and benchmarks; define objective evaluation frameworks and quality gates; run ablation studies to measure impact and optimize data and training effectiveness to support confident product decisions.
• Develop Data Infrastructure – Create and maintain scalable pipelines for ingestion, preprocessing, filtering, and annotation of large, complex datasets, with attention to privacy, governance, and long‑term reuse across security scenarios. 
• Research & Innovation – Collaborate with cross-functional teams to push research and product boundaries, delivering models that make a real-world impact. 
  
  

Qualifications

• M.Sc. / Ph.D. in Computer Science, Information Systems, Electrical or Computer Engineering or Data Science (Ph.D. strongly preferred). Candidates with M.Sc. / Ph.D. in related fields with proven industry experience or a strong publication record in the areas of LLM, Information Retrieval, Machine Learning, Natural Language Processing, Time Series Forecasting and Deep Learning are considered as well.  
• Proven hands-on experience of at least 8 years (including post-grad work) in building and deploying Machine Learning products. Key areas of expertise include Natural Language Processing and Large Language Models, along with an understanding of concepts such as Privacy and Responsible AI. Candidates are expected to demonstrate a strong history of successfully translating applied research into production-ready solutions, along with a proven track record of delivering projects within large-scale production environments.   
• Demonstrated ability to set long‑term technical strategy, align multiple teams, and serve as a technical decision‑maker for high‑risk, high‑impact investments.
• Proven expertise in the LLM and/or time-series forecasting domain, demonstrating comprehensive knowledge of relevant concepts in the domain. Ideal applicants should be proficient in areas such as LLM’s pre and post training, including CPT, SFT and RL, LLM benchmarking, agentic flows, and model alignment.  
• Hands-on experience in building neural model architectures at the 100M+ scale and the proficiency to adapt them at all abstraction levels down the individual block (e.g. changing the innerworkings of an attention block, introducing new blocks, or changing the routings)
• Demonstrated proficiency in problem-solving and data analysis, with substantial expertise in evaluating the performance of large language models (LLMs) and/or time-series forecasting models, developing benchmarks tailored to practical scenarios. 
  
  

Preferred Qualifications

• PhD degree in Computer Science, Information Systems, or Data Science.   
• Evidence of research contributions through publications or records of top-tier journal and conference publications or submitted/accepted papers in top venues (KDD, ICML, AAAI, ACL, ICLR, etc.) 
• Proven track record in pre/post-training of large transformer models for language and/or time series tasks.
• Customer obsession and passion about making real world product impact through production deployed systems.  
• Excellent verbal and written communication skills, with the ability to simplify and explain complex ideas.   
• Effective collaboration skills while working within a globally distributed organization.
  
  

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This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.