Engineering Manager, Autonomous Knowledge

At Lyft, community is what we are and it’s what we do. It’s what makes us different. To create the best ride for all, we start in our own community by creating an open, inclusive, and diverse organization where all team members are recognized for what they bring.

From day one, Lyft’s mission has been to improve people’s lives with the world’s best transportation. And self-driving cars are critical to that mission: they can make our streets safer, cities greener, and traffic a thing of the past. That’s why we started Level 5, our self-driving division, where we’re building a self-driving system to operate on the Lyft network.

Level 5 is looking for doers and creative problem solvers to join us in developing the leading self-driving system for ridesharing. Our team members come from diverse backgrounds and areas of expertise, and each has the opportunity to have an outsized influence on the future of our technology. Our world-class software and hardware experts work in brand new garages and labs in Palo Alto, California, and offices in London, England and Munich, Germany. And we’re moving at an incredible pace: we’re currently servicing employee rides in our test vehicles on the Lyft app. Learn more at lyft.com/level5.

As an engineering manager (EM) and part of the AV Knowledge org, you will be responsible for managing and growing the Scenarios team. This team’s mission is to develop a deep understanding of the real world driving condition and model it in a virtual world.

For this position, we are looking for an EM with strong technical background and hands-on software engineering experience in Applied Machine Learning field.

Responsibilities:


  • Manage and grow the Scenarios team. Lead execution of team deliverables, organize team sprint meetings, and work with the team to achieve Objectives & Key Results (OKR’s)

  • Support setting the mid- and long-term technical roadmap for the team in collaboration with the technical leads and the leadership team

  • Foster a healthy and collaborative team culture and be a role model by demonstrating Lyft’s core values

  • Mentor, coach and grow team members and support them in their career growth

  • Participate in code review and technical whiteboard discussions. Support trading off technical debt versus the pace of progress

  • Triage and prioritize incoming tickets from fleet testing, assigning them to appropriate engineers

  • Collaborate closely with leads and EMs working on different components across the org.


 Experience & Skills:

  • Ability to work with complex, production-quality systems and ability to give constructive code review comments

  • Experience with production machine learning pipelines ranging from large-scale data collection, dataset creation, labeling, training and real-time inference

  • Openness to new / different ideas. Ability to evaluate multiple approaches and choose the best one based on first principles

  • 2+ years of people management experience


 Nice to Have:

  • Demonstrated success in influencing across organizations

  • Ability to communicate highly technical problems and solutions at all levels from engineer to C-level executives

  • Proven ability to operate effectively and autonomously across multiple teams in situations of ambiguity, with only high level direction


Lyft is an Equal Employment Opportunity employer that proudly pursues and hires a diverse workforce. Lyft does not make hiring or employment decisions on the basis of race, color, religion or religious belief, ethnic or national origin, nationality, sex, gender, gender-identity, sexual orientation, disability, age, military or veteran status, or any other basis protected by applicable local, state, or federal laws or prohibited by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Pursuant to the San Francisco Fair Chance Ordinance and other similar state laws and local ordinances, and its internal policy, Lyft will also consider for employment qualified applicants with arrest and conviction records.


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