Staff Machine Learning Engineer

PayPal is looking for an individual contributor with strong ML engineering background in the Global Analytics and Data Science (GADS) Organization to design and develop a suite of machine learning solutions driving large-scale personalization of financial services, merchant products and action recommendations for millions of PayPal customers across the world.

Meet our team

PayPal is a global leader in online payments and democratization of financial services, providing payment solutions for hundreds of millions of customers all over the world. In a high-impact and high-visibility environment, you will have the opportunity of utilizing PayPal’s large-scale infrastructure (including network graph assets) to design and develop large-scale ranking and recommendation systems powering content on novel user interface designs to fundamentally enhance customer experience and engagement.

Your way to impact

As an ML engineer, you will have the opportunity to pioneer large-scale ranking and recommendation systems for sequential content consumption on newly installed user interface designs at PayPal. The solutions developed by you will aid in building novel and meaningful graph-based community assets around the PayPal network of consumers and merchants, to ultimately drive key product and marketing KPIs associated with customer experience, engagement and the revenue bottom line.

Your day to day

As a Staff Machine Learning Engineer you will be responsible for:

• Creating innovative AI/ML solutions that enhance personalization for PayPal users, with a focus on ranking and recommendation algorithms.
• Writing scalable, production-quality code to deploy models on company infrastructure, optimizing for performance and efficiency.
• Collaborating with cross-functional teams, including engineering, product, and marketing, to design, develop, and track key performance indicators (KPIs) for ranking and recommendation models.
• Conducting experiments to measure these KPIs, as well as deriving actionable insights from the data, to continually improve the technology and drive business outcomes.

What are we looking for

• Advanced degree (MS or PhD) in quantitative science or engineering field (for example: Computer Science, Statistics, Mathematics, Operation Research) with a minimum of 5 years of hands-on experience as an individual contributor.
• Proven expertise in designing and developing AI/ML models for ranking and recommendation systems, with in-depth understanding of both traditional collaborative/content-based recommendation methods and cutting-edge deep learning algorithms, reinforcement learning, and bandit techniques.
• Demonstrated ability to write scalable production-quality code in Python, Java, Scala or a similar programming language, and to design and implement data engineering pipelines using technologies like Hive, SQL, BigQuery, or Spark.
• Proficiency in machine learning frameworks and packages, such as Tensorflow and PyTorch.

Nice to Haves

• Experience with Graph-based algorithms and infrastructure.
• Experience working on feed-based ML ranking and recommendation systems.
• Prior experience working in a cloud-based environment such as GCP.
• Hands-on experience with conducting experiments in various areas of personalization and causal inferencing.

We know the confidence gap and imposter syndrome can get in the way of meeting spectacular candidates. Please don't hesitate to apply.