Staff ML Research Lead

As a Staff ML Research Lead you will work on challenging ML projects that extend and improve the way ML is used in our company.
ML is at the very center of RTB House, and you will have a strong, immediate impact on our core metrics.
We are looking for a strong technical leader that will also manage a small team of high performing researchers.
The following skills will be crucial:
Leadership skills.
We're looking for someone who can identify high-potential projects, create a vision, communicate with stakeholders, and above all, execute.Strong technical skills.
We are looking for someone who will lead the projects technically, being enthusiastic about hands-on work.End to end ownership.
You will own the projects from conception, through analysis, research, building a proof of concept, testing, to final implementation and deployment.Effective communication.
In this role you will collaborate with many people, and it's crucial to communicate well and organize the work effectively.Ability to solve unclear problems.
In each project, you will have to understand the core of the issue, define the goal and how to measure success, and find the most effective way to solve the problem.Being results oriented.
We value a healthy drive to solve problems effectively, by starting simple and using complex tools only when necessary, always striving for a simple, elegant solution.Ability to work cross-team.
You will be working cross team, often across IT and business orgs.Ability to build and manage a team.
You will start as the first researcher on a Platform Research Team, with the expectation of growing the team to a couple researchers / engineers in the first year.You will work with following tech stack:
Python, Java, ScalaJupyter NotebooksBigQuery, GCPMust have8+ years of experience as researcher or engineer.3+ years of experience with Machine Learning / Data Science.Proficiency in programming.3+ years experience managing a team.RTB House is a performance oriented company, and our technology is our main strength.
We work with more than 2000 clients across EMEA, APAC, and the Americas.
Our highly optimized system serves nearly 10 million queries per second.
We evaluate multiple models during a request, including deep neural nets - Machine Learning is at the very heart of our operation.
We are constantly improving and extending our ML platform.
Example projects include:
Democratizing experimentation with new signals to a wider set of teams.
Historically, the research on improving our deep learning models has been isolated in our Machine Learning Team.
While the team is very strong, and producing great results, we think there's great potential in allowing our wider organization to experiment with new signals and new features.
This way, we can have many people with various backgrounds and various strengths work on specific verticals of our portfolio of advertisers (electronics, fashion, travel, .
).
This will open up new opportunities, not possible with a single team.
The challenge is to create a system that enables such experimentation, that is accessible, maintainable, and where results are interpretable.Optimizing the creative design process.
Historically, improvements to the graphical design of our ads, or creatives, yielded a high boost to our metrics.
This area still holds a lot of potential, but to realize it, we increasingly need a structured approach to working with creative designers, testing new designs in a meaningful way, separating real gains from testing artifacts.
One challenge is to understand well how our deep learning system picks which creative to show, and making sure it's compatible with the new creative testing framework.Many more, plus anything you can come up with!What we offerCompensation:
an attractive salary and growth opportunitiesTechnology:
access to the latest technologies and real opportunity to use of them in a large-scale and highly dynamic projects; working on extensive and rich datasetsImpact:
your work will be immediately impacting the company's business results and outcomes globally as well as mentor less experienced colleagues in the teamWellbeing:
extremely flexible working conditions - you work when it is convenient for you and devote as much time as you can; you can work fully remotelyCulture:
work in a team of enthusiasts with wide experience in ML who willingly share their knowledge and learnings on a daily basis, no BS environmentPurpose-Driven Work:
being at heart of the system, your growing knowledge and competences will be used in practical applications directly connected to business results
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