Machine Learning Manager - Genai Foundation Models

Booking Booking · Hospitality · Amsterdam, Netherlands · Data Science & Analytics

Manager for a team building and deploying foundation and fine-tuned LLMs for travel-related applications like chatbots, text generation, summarization, and Q&A. Focuses on research, development, and engineering aspects of LLMs, including scalable deployment with minimal latency.

What you'd actually do

  1. Build, Guide and mentor a team of ML scientists and ML engineers in the development, fine-tuning, and deployment of large language models (LLMs) tailored for the travel domain.
  2. Expertise in the engineering aspects of deploying LLMs at scale with minimal latency. This includes optimizing model performance, scalability, and efficiency to meet the demands of real-time, high-traffic applications.
  3. Define and communicate the technical vision and strategy for LLM-related initiatives, ensuring alignment with company goals and customer needs.
  4. Foster a culture of collaboration, innovation, and excellence within the team.
  5. Prioritize work in collaboration with Product Managers, depending on business needs and keeping stakeholders aligned at all times.

Skills

Required

  • 4+ years of experience leading ML teams in NLP or GenAI
  • Advanced knowledge and experience in managing teams developing LLMs
  • Strong expertise in the engineering aspects of scalable LLM deployment
  • MSc with 6+ years of professional experience, or PhD with 4+ years of experience, applying Machine Learning to solve business challenges
  • Master’s, PhD, or equivalent experience in a quantitative field (e.g., Computer Science, Engineering, Mathematics, Artificial Intelligence, Physics, etc.)

Nice to have

  • MSc or PhD thesis work related to NLP
  • keeping up to date with recent breakthroughs in the field
  • get your hands dirty with code when needed
  • mentoring and coaching
  • collaboration with Product Managers
  • technical decisions within your team
  • developing people
  • training, exploration of new technologies, interviewing, onboarding and mentoring colleagues
  • Push for improvements, scaling and extending machine learning tooling and infrastructure, collaborating with central teams

What the JD emphasized

  • leading LLMs
  • Generative AI innovation
  • foundation models
  • fine-tuned models
  • deploying LLMs at scale
  • minimal latency

Other signals

  • leading LLMs
  • Generative AI innovation
  • foundation models
  • fine-tuned models
  • deploying LLMs at scale