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The role focuses on experimenting with and prototyping new methods for applying generative AI to marketing workflows. You will develop proofs of concept and design evaluation systems to improve the performance of agentic AI applications.
We’re looking to add applied AI research engineers to the team. The ideal candidate will have strong probabilistic and quantitative thinking, the ability to be highly creative and experimental with LLM applications, and ground their work in potential customer and product applications.
Our applied research team focuses on finding new and innovative techniques to improve the frontier of what is possible in agentic AI marketing applications, with a particular focus on image and video. We focus less on theory or writing research papers, and more on experimenting, prototyping, and exploring new methods for applying generative AI to help our customers grow their companies.
Example workstreams include:
Quickly iterating and developing proofs of concept (POCs) to explore the maximum potential of integrating AI into data and marketing workflows
Design and develop evaluation and improvement systems that can make advancements autonomously
Expanding the frontier of what is feasible with AI generated video (e.g. making generated humans maximally realistic)
Experiment with different approaches for generating brand assets that stay aligned to a particular company’s look and feel
We're seeking talented, intellectually curious, and motivated individuals interested in exploring the forefront of AI. While no prior experience with LLMs and AI is required, strong technical skills, particularly in backend architecture or probabilistic systems, and product intuition are essential for understanding and executing these projects from start to finish.
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 $180,000 - $400,000 USD per year, which is location independent in accordance with our remote-first policy. We also offer meaningful equity compensation.
Our interview process focuses on evaluating fit for the most important dimensions of the role: product sense and creativity with LLMs, ability to architect backend 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.
Recruiter Screen [30m]: Introductory call with our recruiting team to get to know each other and see if the role could be a good mutual fit.
System Design Screen [60m]: Designing a data processing or machine learning feature end-to-end (depending on background).
Hiring Manager Interview [30m]: Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles.
Agent Building Systems Interview [75m]: Work with the interviewer to architect an agentic 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.
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