Principal/Senior Applied Scientist Security Models Training Team - Next-Gen frontier research Job at Microsoft Corporation (Tel Aviv, Herzliya)

Job Description

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 a Principal/Senior Applied Scientist, you will own end‑to‑end model development for security scenarios, 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, and mentoring 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.

Job Responsibility

Technical Leadership & Ownership: set technical direction for major security domain initiatives
lead security model programs spanning pre‑training, task tuning, reinforcement learning, and evaluation
translate cutting‑edge research into production‑ready capabilities
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

Requirements

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 5 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
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

Nice to have

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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Principal/Senior Applied Scientist Security Models Training Team - Next-Gen frontier research

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