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Hightouch
Hightouch is seeking a Machine Learning Engineer to expand their data activation products with an intelligence layer. The role involves personalization, automated experimentation, predictive audiences, content generation, and budget optimization. As part of the team, you will work on customer research, problem definition, predictive modeling, machine learning infrastructure, and partner with customers. We are looking for motivated individuals interested in these challenges. This is a senior role with a salary range of $200k to $260k USD per year.
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.
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.
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. The problem will be at a pretty high level - and have both product and customer requirements as well as technical.
Hiring Manager Interview [30m]: Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.
