Senior Manager, Applied AI Research

As Senior Manager, Applied AI Research, you will provide hands-on technical leadership across a portfolio of applied AI initiatives that protect over 500 million users worldwide, spanning Small Language Models, reinforcement learning, agentic systems, computer vision and classical machine learning.

You will shape the architecture, modeling choices and production path for these programs while managing and growing a small team of 3-5 Research Engineers responsible for end-to-end delivery of applied AI, from problem framing and data curation through model training, customization and evaluation to deployment, monitoring and iteration at massive scale.

This role blends technical leadership with people management. You will lead the team technically by setting direction, shaping architectures and staying close to the models and code, while also growing engineers and owning delivery. You will ensure that the models your team builds reach production and contribute to Gen’s cybersecurity mission.

- Provide hands-on technical leadership across multiple applied AI workstreams, including Small Language Models, reinforcement learning, agentic AI, computer vision and classical ML.

- Drive architecture, modeling choices and productionization decisions with your team of Research Engineers.

- Manage, mentor and grow a small team of 3-5 Research Engineers, including hiring, performance management, career development and day-to-day technical coaching.

- Keep the team focused, unblocked and outcome-oriented.

- Personally contribute to the hardest parts of the work by prototyping novel approaches, reviewing and improving model designs, and guiding training-run debugging on GPU clusters.

- Set the bar for research and engineering quality across the team.

- Own the full applied-AI lifecycle for your area, from problem framing and data strategy through model training, fine-tuning and evaluation to deployment, observability and continuous improvement in production systems serving 500M+ users.

- Champion an “always ship to production” team principle and partner with Platform, MLOps and Product Engineering teams to ensure models are deployed, monitored and measurably improving user outcomes.

- Translate business and security priorities into a concrete applied-AI roadmap and sequence priorities across multiple domains.