Senior Manager, Machine Learning Engineering

Indeed•Remote,

1d•$193,000 - $402,000•Remote

About The Position

At Indeed, our mission is to Help People Get Jobs. Marketplace Efficiency connects the right jobseekers with the right jobs and gives employers qualified candidates for their spend. The team balances the needs of jobseekers, employers, and overall marketplace health. Our levers are ranking, recommendation, auction and bidding, pricing, and budget optimization across Indeed's sponsored jobs advertising business. As the Senior Manager of Machine Learning Engineering on Marketplace Efficiency, you will lead multiple teams of machine learning engineers and data scientists. These teams include managers and senior technical leads. You will set the technical direction for the models and systems that decide which jobs appear, in what order, and at what price. These decisions affect millions of jobseekers and employers every day. You will partner with Product, Software Engineering, Data Science, and business leaders. Together, you will turn marketplace strategy into a roadmap, run rigorous experiments, and deliver measurable results for jobseekers, employers, and Indeed. AI and macroeconomic shifts are changing the labor market quickly. This role has a unique opportunity to shape how Indeed's marketplace adapts to meet that challenge. You will be able to work from anywhere within the United States for this role.

Requirements

Requires a Bachelor’s degree in Computer Science, Mathematics, Statistics, or related field and a minimum of 12 years of related experience; or a Master’s degree with a minimum of 8 years of experience; or a PhD with a minimum of 5 years experience
Proven success as a manager of machine learning engineering or data science teams, including managing multiple teams or other managers, and a track record of coaching engineers and managers, growing careers, and building inclusive, high-performing teams.
Experience delivering complex, large-scale machine learning systems to production, with a deep understanding of machine learning, statistical modeling, and the tradeoffs of deploying ML at scale.
Experience building ranking, recommendation, or matching systems at scale, as well as experience with auction design, bidding, pricing, or budget optimization in an advertising system or marketplace.
Experience running large-scale causal experiments.
Well-versed in coding (Python, Java, Go, or C++), SQL engines such as Presto or Trino, and data processing frameworks such as Spark.
Proven ability to turn complex data findings into business strategy and influence product, technical, and business direction across a large organization, with effective and inclusive verbal and written communication for technical, business, and executive audiences.

Responsibilities

Manage and grow multiple teams of machine learning engineers and data scientists, including first-line managers and senior technical leads. Hire, coach, and develop talent, and build a strong bench of future managers and technical leaders.
Own the machine learning roadmap for ranking, recommendation, auction, and pricing systems, aligning the roadmap with Marketplace Efficiency and company goals.
Define marketplace objective and guardrail metrics that trade off jobseeker, employer, and marketplace-health outcomes, and hold the teams accountable to these metrics.
Raise the bar on experimentation by improving A/B testing methods for a two-sided marketplace, including interference, budget effects, and long-term outcomes.
Drive the delivery of large, cross-team initiatives by setting clear expectations for scope, timelines, and quality, and delivering predictable results. Translate business goals, such as advertiser return, revenue, and jobseeker engagement, into machine learning solutions with Product, Engineering, and business partners.
Represent Marketplace Efficiency in senior leadership forums, communicating results, risks, and tradeoffs to technical and executive audiences.
Contribute directly when needed by reviewing designs, diving into code or data, and sharing best practices across the organization.