VP of Engineering

LeapYear is looking for a seasoned and deeply technical senior executive to lead and scale the engineering organization. 

Reporting to the CEO and working closely with the leadership team, the Vice President of Engineering will take over the day-to-day engineering and technical leadership for LeapYear.  

This role can be based in the San Francisco Bay Area.

  • Rapidly scale engineering organization
  • Oversee algorithms research, machine learning infrastructure, front-end development, and QA
  • Ensure regular delivery of high quality, enterprise-ready features and products
  • Recruit and develop top technical talent 
  • Collaborate with CTO on architecture
  • Provide technical authority in sales discussion with C-level IT executives
  • Provide architectural and implementation advice to customers and partners
  • Contribute to overall business strategy

  • Extensive track record of successfully delivering enterprise infrastructure technologies
  • 10+ years of director or VP-level engineering management experience with a track record of managing through managers and running the entire engineering organization
  • Proven ability to rapidly scale engineering organizations at high growth companies while maintaining the highest standards of talent, product quality, and culture
  • Strong organizational management skills, with a toolkit of processes and best practices ready on day one
  • Strong communication with both technical and non-technical stakeholders, such as customers, executives, employees, recruiters, and vendors
  • Deep understanding of and experience implementing information security best practices
  • Strong entrepreneurial mindset and recent startup experience

  • Experience managing research scientists
  • Experience managing front-end development teams
  • Extensive network of technical talent
  • Knowledge of modern and legacy enterprise IT environments
  • Experience developing and deploying machine learning architectures at scale
  • Experience with big-data technologies, such as Hadoop and Spark
  • Understanding of functional programming paradigms
  • Hands-on experience developing enterprise products with deep theoretical underpinnings
  • Mathematical depth sufficient to understand academic literature in machine learning
  • Masters or PhD in in machine learning or related field (mathematics, computational physics, statistics, etc.)

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