Machine Learning Engineer, Ads Contextual Intelligence

Key Skills

Apache Airflow, Apache Cassandra, Apache Flink, Apache Kafka, DynamoDB, Kubernetes-K8s, Machine learning techniques, MongoDB, Python Programming, REDIS, Ruby on Rails, SPARK Programming

Job Description

Reddit is continuing to grow our teams with the best talent. This role offers flexibility and is remote-friendly, However, it is important that the candidate is based in Amsterdam.

The Ads Contextual Intelligence team is the result of a recent merger between Reddit and Spiketrap. This newly formed team is responsible for all the “contextualization” projects within the Reddit Monetization charter. We are in charge of making sense of all users and advertisers generated content in an automated fashion at scale, providing the right combination of signals to improve marketplace efficiency and bringing to the surface Business Intelligence insights. Our team has the potential to highlight one of Reddit's biggest differentiators: genuinely curated, high quality, extremely relevant, and daily updated organic content. We are a Machine Learning/Data heavy team with a focus in the following areas:

• Knowledge Graph - Semi-automatic curation, expansion, and utilization of Reddit very own Knowledge database. NLP tokenization, NER models, disambiguation systems at KG node level.
• Contextual Signals - Make sense of Posts, Landing pages, Ads Placements, Organic Topics using team owned engines in combination with other Reddit’s content understanding teams.
• Marketplace Efficiency - Helping Targeting, Prediction, and Quality deliver best in class performance leveraging our Contextual Signals
• Business Intelligence Applications - Bring Contextual Signals (Web, APIs, Custom Reporting) to our Advertisers to empower a new generation of Business Intelligence and help Brands understand Reddit better

Responsibilities

• Developing new or iterating on existing ML-NLP engines including NER, text classification, semantic vector spaces, bot detection, clustering.
• Collaborate with the Contextual platform group to build data processing pipelines that leverage the ML engines 
• Collaborate with peer ML Engineers and embedded Data Scientists: you will help pushing the boundaries of our current capabilities and figure out ways to keep track of predictive performance
• Building and maintaining APIs to expose ML-based inferences services
• Ensuring the reliability, scalability, and performance of the ML systems by writing automated tests, monitoring performance, and implementing best practices for model management
• Participating in modeling and coding reviews: You will review work by other team members and provide feedback to ensure that it meets the team's standards for quality and performance.
• Collaborating with cross-functional teams to understand business requirements and translate them into technical solutions.

Required Qualifications:

• 2+ years of industry experience as a Software Engineer developing production code
• Experience with at least one general programming language such as Ruby or Python
• Preferred Experience in one or more of the following areas: Message Queuing Services (Kafka, Amazon Simple Queue Service), Data Processing Frameworks (Apache Spark, Apache Flink), Key Value Stores (Redis, DynamoDB), Document based DBs (MongoDB,Cassandra), Container Orchestration (Kubernetes, Mesos)
• Familiarity with algorithms and data structures, and the ability to apply them to solve complex problems.
• Excellent communication skills, both written and verbal, and the ability to work effectively with product managers, data scientists, and other stakeholders
• A willingness to learn and stay up-to-date with SOTA ML technologies and best practices in the field.

Preferred Qualifications:

• Experience with Python, Ruby, Rails, Kafka, Airflow, Kubernetes
• Experience with Ads
• Experience with Natural Language Understanding (ML science and infra)
• Experience building scalable data-driven systems

Benefits:

• Pension Savings plan 
• Medical Plan 
• Short term sickness benefits 
• WIA excess and WGA gap insurance 
• Workspace benefits for your home office
• Personal & Professional development funds
• Family Planning Support 
• Flexible Vacation & Reddit Global Days Off