Machine Learning Engineer, AI Decisioning

About Hightouch

Hightouch’s mission is to empower everyone to take action on their data. Hundreds of companies, including Autotrader, Calendly, Cars.com, Monday.com, and PetSmart, trust Hightouch to power their growth. We pioneered the Composable Customer Data Platform (CDP), which lets companies use their own data warehouse to collect, prepare, and activate customer data for marketing personalization and business operations. Our new AI Decisioning platform goes a step further, allowing marketers to set goals and guardrails that AI agents can then use to personalize 1:1 customer interactions.

Traditionally, only technical teams had the skills to access and use customer data. With Hightouch, every business user can deliver personalized customer experiences, optimize performance marketing, and move faster by leveraging data and AI across their organization. Our team focuses on making a meaningful impact for our customers. We approach challenges with a first-principles mindset, move quickly and efficiently, and treat each other with compassion and kindness. We look for team members who are strong communicators, have a growth mindset, and are motivated and persistent in achieving our goals.

About the Role

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 figure 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: Helping 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: Assisting marketers in generating compelling text, images, and creatives.
• Budget Optimization: Assessing which marketing spend is driving the most incremental conversions.

As an early machine learning engineer, you will help build comprehensive solutions to the above domains from scratch. Responsibilities will be highly varied and include working on customer research, problem definition, predictive modeling, machine learning infrastructure, and partnering with customers. We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $200,000 - $260,000 USD per year, which is location independent in accordance with our remote-first policy.

Interview Process

Our interview process focuses on evaluating fit for the most important dimensions of the role: product sense, ability to architect backend and distributed systems, and alignment with Hightouch’s values. Notably, we don’t do any programming interviews as we believe they are low signal to noise and aren’t a good evaluation mechanism.

• Intro Call (15-30m): Introductory call with either a member of our recruiting team or the hiring manager to get to know each other and see if the role could be a good mutual fit.
• System Design Screen (45m): Designing a data processing feature end-to-end.
• Machine Learning Modeling Interview (90m): Designing a predictive model end-to-end, including data collection and preparation, model training and evaluation, and what systems would be needed to run the model in production.
• System Design Interview (90m): Work with the interviewer to architect a system at a conceptual level.
• Hiring Manager Interview (30m): Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.