Lead Machine Learning Engineer

New York • $160k - $190k • 0.01% - 0.01%

Beeswax


Role Location

  • New York

Compensation

  • $160k - $190k
  • 0.01% - 0.01%

Employees

51 - 100 people

Address

275 7 Th Ave Fl 21
New York, NY, 10001, US

Tech Stack

  • Python
  • C++
  • JavaScript
  • MySQL
  • Snowflake
  • Aerospike
  • PHP
  • React

Role Description

Beeswax is looking for a Lead Machine Learning Engineer to join our growing team. We were recently recognized on the Inc. 5000 list as #46 in the fastest growing companies and #5 in the top software companies. In 2018, we were also named by Business Insider as the “fastest growing company in AdTech"

Beeswax is an easy to use, massive scale and highly available advertising platform founded by industry veterans who worked together at Google. We’re well funded by leading VCs, such as RRE and Foundry Group, and are rapidly expanding our customer list and our engineering team. We offer our customers the most extensible and transparent advertising platform in the world and process millions of transactions per second.

Our engineers come from major tech companies such as Amazon and Facebook as well as many other companies with strong software disciplines. We take pride in our mission to build great advertising software.

The Optimization team at Beeswax is simplifying Real Time Bidding for our customers and making it easy for them to train and deploy machine learning models on our platform. To do that, we want to build systems that provide customers easy access to their data, algorithms to train their models and APIs to easily deploy and evaluate the performance of their models. We’ll know we’re successful when we see our customers actively using our ML workflow and bidding with much higher success and effectiveness rates when using these tools.

We are looking for a Lead Machine Learning Engineer for our Optimization team. The ideal candidate will have experience working on a range of optimization problems in a production environment, such as click-through rate prediction, click-fraud detection, payment fraud, search ranking, text/sentiment classification, viewability prediction, or spam detection.

Your primary role will be to lead and grow a machine learning team to support both internal feature development and the creation of a framework our customers can use.

Responsibilities

Work with customers and the product team to design optimization systems for both off-the-shelf use and customization through APIs. Build and iterate on a workflow that enables our customers to take advantage of our data and ML infrastructure. Develop highly scalable machine learning systems to automatically score and optimize real-time bidding advertising campaigns.

Ideal candidates will have:

A minimum of 5 years experience building ML infrastructure and production models in a product driven driven environment. Proficiency with statistics and statistical methods. Experience with scripting languages such as Python and libraries like Numpy/Pandas. Experience using machine learning libraries or platforms including Tensorflow, Caffe, Scikit-Learn, ML lib in production. Experience with data warehouses such as Snowflake, Redshift or Presto and data processing platforms such as Spark. Experience with stream processing such as Kinesis or Kafka is a plus. AdTech experience is a plus.

About Beeswax

We're SaaS to let advertisers connect directly to ad exchanges to participate in real-time ad auctions. In the same way Ameritrade and other internet brokers made it much easier for people to trade stocks, we make it much easier for advertisers to advertise online via our APIs.

Company Culture

We're an engineering culture at our core. We want all our engineers to have: An ethic of service and a belief in putting the customer first. A powerful sense of pragmatism to figure out what needs to be done right versus right now. A curiosity about technology and a desire to use it to solve problems in all sorts of domains. An openness to feedback and more than just the spelling skills to know that there’s no I in team. An appreciation of repeatability, resilience, observability, and operational simplicity.

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