Senior Machine Learning Engineer (Beeswax/AdTech) Offline

CUSTOMER

Our customer is a rapidly growing US AdTech company Beeswax. Founded by three ex-Googlers, it has a highly technical team and an excellent technological culture.

Beeswax provides extremely high-scale Bidder-as-a-Service solutions in advertising technology, works with global businesses, and has to date raised $28M (incl. the most recent Series B raise of $15M).

Sigma Software works together with Beeswax to enable the delivery of numerous key components of the platform and is looking for engineers to complement Beeswax engineering team and to drive further development of the platform.

PROJECT

The project is about building the next generation of the real-time bidding software that enables sophisticated marketers to break free from the limitations and constraints of opaque, one-size-fits-all programmatic buying platforms.

In this role you’ll work on a wide range of computational challenges such as: CTR prediction, forecasting, bidding models, and delivery optimization. Our client practices an effective data science process that recognizes the uncertainty in explorations for ML solutions but also emphasizes an agile software engineering workflow. Beeswax uses the most popular tools in the Python data science ecosystem and embraces innovation.

The tech stack is always evolving to meet the challenges of the massive scale of transactions, on which we operate. To manage the firehose of data coming in, the team explores complex tradeoffs and carefully architects high performance distributed systems. Those in turn require elegant and thoughtfully designed interfaces to make the systems accessible to both Beeswax team and their customers.

REQUIREMENTS

Strong experience in the Machine Learning/Data Science industry

Strong understanding and experience in distributed computing frameworks, especially in Apache Spark

Strong-level proficiency in Python and SQL

Practical experience with Data Warehousing (especially Snowflake), Deep Reinforcement Learning, and out-of-core algorithms

Good understanding of engineering best practices, agile (as it applies to Data Science), and version control

Effective collaboration with Product, Customer, and Engineering teams

At least Upper-Intermediate level of English (both spoken and written)

RESPONSIBILITIES

Actively participate in data science projects and ensure they deliver business value

Work with the Product and Engineering team to build data model pipelines to power customer-driven products

Drive architectural changes to improve engineering velocity

Implement and test the latest research works