Twitter-Principal ML Applied Researcher – META

San Francisco, California, United States;New York, New York, United States

About the job:

Who we are:

We are a group of researchers and engineers working to help Twitter improve how we apply machine learning in a range of impactful systems, such as recommendations, safety, abuse detection, content understanding, and advertising. We investigate these systems at scale with the goal of anticipating, discovering, and mitigating any harmful impact they might have on our global community.

We believe in the power of bringing diverse perspectives together. Our team operates at the intersection of machine learning, the social sciences, policy, legal and user research in collaboration with numerous partners from across Twitter.

What you will do:

You will apply your research expertise to help understand the implications of automated decision systems, as well as related societal and representational harms. Work with the team to conceptualize difficult problems, devise measurement and audit methodologies, work toward effective interventions, and propose more inclusive and fair alternatives to existing practices. 

You’ll partner with other META leaders (EM, PM, Sr Engineers) and work with our product teams, researchers, and engineers to build a high-value roadmap of innovation for your organization. You will lead by example to build your team culture in keeping with Twitter’s culture. You will partner with Engineering Managers in multiple organizations across Twitter and guide engineers with your technical expertise by contributing to design reviews and ideation. You will help mentor, coach and grow people.

You will lead research projects to enable Twitter to better apply machine learning on its platform in a way that benefits all our customers and society at large. You will be contributing to strategic decisions and future roadmaps for products and technologies at Twitter.

Responsibilities:

Who You Are: 

  • You look ahead to identify strategic opportunities and cultivate a culture of innovation. You have a track record of establishing long-term visions for teams and making them successful against those visions. 
  • You bring unwavering customer focus, and an opinionated perspective that inspires change and motivates engineers to develop simple solutions to complex problems.
  • You have excellent communication and collaboration skills. You articulate desired outcomes clearly, and are able to build organizational alignment in a complicated space.
  • You are experienced in building both organizations and distributed systems that scale. You’re confident in leading leaders and passionate about developing them, and you’re committed to growing diverse and inclusive teams.

Requirements:

  • Post-graduate or PhD in a relevant field, including (but not limited to) Computational Social Science, Economics, Political Science, Public Policy, Sociology and Computer Science.
  • 10+ years machine learning research experience broadly construed, and at least a few years of research experience in ML fairness.
  • 3+ years of independent research career, e.g. as faculty in a university or senior researcher in the industry
  • Ability to mentor and lead others
  • Excellent communication skills, both with technical and non-technical audiences
  • Strong theoretical grounding in core machine learning concepts and techniques
  • Proven ability to translate research into practical outcomes
  • Evidence of independence, originality and creativity in research
  • Excellent publication record in top conferences in the ML fairness or related fields
  • Recognition in the research community, as evidenced by e.g. invited keynote talks, program committee, or editorial board membership
  • Strong proficiency in Python or R or SQL

Nice to haves:

  • Experience as an academic faculty member
  • Experience in an industrial research lab or a startup
  • Experience with student supervision (and ideally successful graduates in the industry/academia)
  • Experience with large-scale systems and data, e.g. Hadoop, distributed systems