Engineering Manager - Machine Learning

Adobe Adobe · Enterprise · Bucharest, Romania

Engineering Manager for Adobe's Express AI Pillar team, focusing on AI foundations, ML features, infrastructure, model hosting, delivery, and ethical dataset creation. Requires strong leadership, ML experience, and ability to manage teams.

What you'd actually do

  1. Lead and manage a team of up to 10 people;
  2. Guide team through research, technical design, implementation, delivery, and maintenance of the application.
  3. Influence design decisions and technical direction for engineers, researchers, product managers and senior leaders.
  4. Ensure transparency of project status to partners
  5. Lead each team member’s performance, employing ongoing performance feedback and coaching. Develop and evolve engineering processes and collaboration models to optimize team efficiency.

Skills

Required

  • BS or MS in Computer Science or a related field
  • 5+ years of experience as an engineering manager, leading teams that built complex applications
  • 7+ years of experience as on the ML field with consistent and state-of-the-art results
  • Experience in building and leading teams with multiple ML engineers and researchers
  • Demonstrated ability to build a diverse team of top engineers and ML researchers in a competitive job market
  • Strong analytical and problem-solving skills
  • Strong working knowledge of modern software development and research methodologies and software design patterns
  • Outstanding ability to communicate technical concepts effectively to partner teams
  • Strong customer empathy and demonstrated engagement with customers and partners to truly delight users

Nice to have

  • Knowledge and/or experience building machine learning applications are a big plus

What the JD emphasized

  • 7+ years of experience as on the ML field with consistent and state-of-the-art results
  • Experience in building and leading teams with multiple ML engineers and researchers.

Other signals

  • leading development of ML features, infrastructure and foundation
  • managing model hosting, delivery infrastructure
  • ethical dataset creation