Engineering Manager, Machine Learning - Revenue Science - Ads
San Jose
hace 4 días

The Position

We are a geographically-distributed team of Machine Learning Engineers that are responsible for placing each and every ad that Twitter serves.

We focus on optimizing campaign performance and ROI for performance-minded advertisers, using modern ranking and personalization techniques.

We prioritize impact, routinely deliver material improvements to the companys ads revenue, and work across the entire ads ecosystem.

Technically, we are involved in the full array of modern applied machine learning work, including ideation, experimentation, implementation, and maintenance.

This includes work across our ads stack on predictive modeling, improving the way the system explores new traffic, mitigating effects of selection bias, effective budget pacing, efficient AB testing, accurate candidate ranking, visualizations, and more.

You are an engineering manager with a strong background in data science, machine learning, and ad-tech. You are principled and are looking to play a critical role at a very public company operating a multi-billion dollar business.

You have a track record of establishing long-term vision for the team and then making them successful against that vision.

You lead, manage and mentor contributors on your team. Youre skilled at communicating the results of technical work to executives, as well as proactively changing priorities and tactics for your team in response to strategic changes at the executive level.


  • Lead a team of motivated engineers who like to build models, ship product integrations, and tackle hard engineering problems.
  • Build cohesive, high-functioning teams that thrive in a culture of trust, respect, and inclusion.
  • Balance autonomy with guidance by giving your teams the tools, context, confidence, and motivation to make decisions effectively and independently.
  • Have the technical capacity to partner with tech leads and be comfortable diving into the fray to help drive resolution in the case of bad incidents.
  • Take responsibility for the groups short-term and long-term strategy. Define the team's roadmap, success metrics, and priorities in close collaboration with other engineering teams and cross-functional partners.
  • Maintain a balance between building sustainable, high-impact projects and shipping things quickly.

  • Own your teams deliverables and ensure we continue to ship scalable, highly-available products that delight our users and customers.
  • Work closely with the Twitter recruiting team to hire high-potential candidates from diverse backgrounds.
  • Minimum Qualifications

  • 1+ years of experience managing a team of Machine Learning Engineers or Modelers.
  • 4+ years of industry experience with Applied Machine Learning.
  • Familiarity with fundamentals of Machine Learning and Data Science.
  • Experience driving impact through cross-functional leadership.
  • Exceptional written and verbal communication skills.
  • Bachelors in Computer Science, Mathematics or related quantitative field.
  • Preferred Qualifications :

  • Industry experience with Online Advertising and / or Large-scale Distributed Systems.
  • Masters or PhD degree in Computer Science, Mathematics or related quantitative field.
  • Company Description

    Twitter is whats happening and what people are talking about right now. For us, life's not about a job, it's about purpose.

    We believe real change starts with conversation. Here, your voice matters. Come as you are and together we'll do what's right.

    Additional Information

    We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran, genetic information, marital status or any other legally protected status.

    San Francisco applicants : Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records

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