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Software Engineering Manager – Computer Vision and Machine Learning

Software Engineering Manager – Computer Vision and Machine Learning

CompanyMeta
LocationPittsburgh, PA, USA
Salary$177000 – $251000
TypeFull-Time
DegreesBachelor’s, Master’s
Experience LevelSenior, Expert or higher

Requirements

  • BS/Masters in Computer Science, Engineering, Physics or equivalent practical experience
  • 8+ years or PhD + at least 4 years, of experience in leading teams productizing machine perception technologies (i.e., computer vision, deep learning, sensors and robotics)
  • 2+ years of experience in managing a team of engineers of varied skill levels
  • Demonstrated experience recruiting, building, structuring, and leading technical organizations, including performance management
  • Experience supporting, coaching, mentoring, and developing software engineers

Responsibilities

  • Lead and mentor the engineering team, fostering their growth and helping them achieve impactful results
  • Promote engineering and operational quality by establishing metrics and processes for continuous assessment and improvement
  • Facilitate cross-functional communication with various teams, organizations, and stakeholders to advance engineering initiatives
  • Collaborate with partners across hardware, electrical, mechanical, software, optical, and ML domains to understand the system architecture necessary for infrastructure development
  • Leverage technical expertise and cross-functional relationships to set strategic engineering goals, intermediate milestones, and drive progress on these goals
  • Use strategic influence and cross-functional relationships to drive progress on goals
  • Recruit engineers by identifying talent gaps and the necessary individuals to fill them
  • Develop career plans, manage performance, and conduct performance reviews

Preferred Qualifications

  • Experience with Computer vision, Machine Learning, Statistics, applied mathematics, data science
  • Experience partnering with research scientists
  • Experience in transferring research to production