Machine Learning Engineer, AI Decisioning

Hightouch; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

A leading data activation company in the US is hiring a machine learning engineer to enhance its products with an intelligence layer. The role involves tackling challenges like personalization, automated experimentation, predictive audience modeling, content generation, and budget optimization. Ideal for candidates eager to leverage data to improve marketing strategies and enhance customer engagement.

Responsibilities

• Work on personalization and product recommendation to enhance user messaging.
• Automate experimentation for messaging options.
• Build predictive models for user actions.
• Explore content generation using recent advances in LLMs.
• Optimize budgets for marketing spend and conversions.

Job description

We’re looking to hire a machine learning engineer as we expand our data activation products to include an intelligence layer. While hundreds of companies use Hightouch today to sync data into their SaaS systems to automate and improve operations, there’s a lot of surface area we haven’t touched in helping companies figuring out which customers to message, what content to put in messages, and when to send messages. A lot of this work today is done manually through intuition and guesswork, and we believe that adding machine learning could have a step function impact for our customers. And given our access to data warehouses and databases, Hightouch is perfectly placed to make use of a company’s customer data in building a powerful intelligence layer.

Some of the problems we’ll be working on include:

• Personalization and Product Recommendation: There are often many options for what content a company could message a user with, including which products to show from catalogues. Given this large state space, how can Hightouch help personalize messages with the most relevant content for each user?

• Automated Experimentation: Helping companies intelligently navigate and automate experiments across the extensive number of options for messaging customers.

• Predictive Audiences: Building models to predict which users are most likely to convert, churn, or take desired actions.

• Content Generation: Particularly with recent advances in LLMs, how can we help marketers generate text, images, and creatives that are compelling to their customers?

• Budget Optimization: Helping companies assess which marketing spend is driving the most incremental conversions, and where the marginal CAC is lowest.