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Gen
Gen
5,001 – 10,000 Employees
B2CCybersecurityFintech
Gen is a global consumer technology company operating across cybersecurity, digital privacy, identity protection, and financial wellness. Through brands such as Norton, Avast, LifeLock, and MoneyLion, it helps people protect their devices, identities, personal information, and finances. Its products include threat detection, breach response, online privacy tools, banking features, and embedded finance services through Engine. Gen also invests in AI-driven security and its Gen AI Foundry, focused on building trust for emerging AI applications. The company serves nearly 500 million users across more than 150 countries and has dual headquarters in Prague and Tempe.
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Senior Manager, Applied AI Research - Prague Hybrid

Lead applied AI research at Gen across language models, reinforcement learning, computer vision and related methods for cybersecurity products. Manage Research Engineers and move models from experimentation into production at global scale.

CZCzechiaHybridFull-timeSeniorEnglish RequiredAI Research ScientistMachine Learning Engineer
CloudCyber SecurityKubernetesPythonPyTorchTensorflow

Description

  • Lead hands-on applied AI initiatives spanning small language models, reinforcement learning, agentic AI, computer vision and classical machine learning
  • Own architecture, modeling and productionization decisions
  • Manage, mentor and develop a team of 3-5 Research Engineers through hiring, performance management, career development and technical coaching
  • Keep the team aligned, unblocked and focused on measurable outcomes
  • Prototype new approaches, refine model designs and support debugging of training runs on GPU clusters
  • Define and maintain research and engineering quality standards
  • Oversee the applied AI lifecycle from problem definition and data strategy through training, fine-tuning, evaluation, deployment, observability and ongoing improvement
  • Drive production delivery of models and collaborate with Platform, MLOps and Product Engineering teams
  • Turn business and security priorities into an applied AI roadmap with clear sequencing
  • Explain trade-offs to senior leaders and report on delivery, model performance and impact
  • Build team practices around experiment tracking, model evaluation, reproducibility, code review and responsible AI
  • Participate in interviews with the Hiring Manager, AI Research Lab, Product, stakeholders and Leadership

Requirements

  • M.Sc. or Ph.D. in Computer Science, Machine Learning, Mathematics or a related quantitative discipline, or equivalent practical experience demonstrating deep-learning expertise
  • At least 5 years of hands-on experience building and deploying machine-learning and deep-learning systems in production
  • 2-3+ years leading or managing a small team of ML or Research Engineers, including technical direction, delivery, hiring and people development across multiple applied AI domains
  • Deep practical expertise in several applied AI areas, including transformer-based small language models, reinforcement learning such as PPO, DQN and Actor-Critic variants, agentic or tool-using LLM systems, computer vision or classical machine learning
  • Ability to match each problem with an appropriate technical approach
  • Expert Python and PyTorch skills; JAX or TensorFlow experience is an advantage
  • Experience training and fine-tuning models on multi-GPU clusters with DeepSpeed, FSDP or Hugging Face Accelerate
  • Strong grounding in linear algebra, optimization, probability and statistics
  • Ability to interpret, assess and implement methods from recent research papers
  • Demonstrated success bringing AI models into production, including data pipelines, evaluation harnesses, quantization or compression, inference optimization, containers, Kubernetes, cloud platforms and post-deployment monitoring
  • Experience with MLOps and experiment-management tools such as Weights & Biases, MLflow or comparable platforms
  • Experience with CI/CD, code review and observability practices
  • Ability to lead a small technical team by setting direction, conducting 1:1s, providing feedback, hiring engineers and converting ambiguous business problems into deployable AI systems
  • Fluent English
  • Hands-on technical leadership approach with a willingness to remain close to code, models and production systems
  • Player-coach mindset
  • Strong focus on shipping, deploying and measuring models in production
  • Sound judgment when assessing applied AI opportunities
  • Clear communication skills for turning complex ML concepts into decisions and trade-offs
  • Ability to work effectively in a fast-paced, ambiguous, high-technology environment while managing complex cross-functional challenges
  • Publications at leading ML venues, cybersecurity, privacy or identity experience, and JAX or TensorFlow expertise are desirable

Benefits

  • Flexible working arrangements
  • Paid time off
  • Competitive compensation
  • Employee benefits
  • Well-being programs
  • Support, resources and tools for strong performance and career development

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