Member of Technical Staff, Evals and Post-Training

Description

Profound is the fastest-growing AI marketing platform, used by 15% of the Fortune 500 including Comcast, Estée Lauder, and Walmart, and innovators like Ramp, MongoDB, and Figma. It is where marketing teams do the work: research, briefs, content, campaigns, reporting, all in one place, alongside agents that carry the load with them.

Built on 2B+ real user prompts and growing, Profound’s data shows you what people ask AI and what the models say back. Profound's AI Marketer joins that data with deep context about our customers to find the opportunities worth acting on, and then puts agents to work: automating reporting, drafting content, auditing pages for readability, running competitive research. It measures what works and gets better every day.

As a Member of Technical Staff in our Evals and Post-training team, you’ll build the evaluation and measurement systems that tell us how good AI (including Profound's harness, known as Aim ) is at marketing tasks. These evals guide what we ship, and they are the foundation for our post-training initiatives: the benchmarks, graders, and environments you build become the signal our models are trained against.

Responsibilities

• Build Profound’s eval system: tasks, rubrics, LLM judges, programmatic verifiers, regression alerts, and self-improvement loops where we look at the traces when something is wrong and come up with the fix
• Define what a great AI Marketer run looks like, and analyze trajectories across multi-step tool use, analytical correctness, and content quality
• Work with marketing experts and data labeling companies to define tasks, metrics, rubrics, and other grading mechanisms
• Close the loop from usage: input use cases, cluster failure modes, and turn these into reliable evals
• Turn eval results and expert feedback into training signal: rewards, preference data, and training environments
• Build tooling for debugging model and agent behavior
• Make evals part of how we decide things: wired into CI, launch reviews, and model selection

Requirements

• 2+ years building evaluation or measurement systems, whether AI evals, experimentation, ranking, or search quality
• Strong Python and data skills, with a track record of shipping production systems
• Deep familiarity with LLM evaluation: LLM-as-judge, benchmark design, agent harnesses, calibration, and understanding how to integrate them into professional settings
• Sound experimental design, and the instinct to separate signal from noise in non-deterministic systems
• Familiarity with modern post-training methods: SFT, preference optimization, and RL
• Strong opinions about how models and agents should behave, and keeping up with current research
• Comfortable operating in a fast-paced startup environment with rapid iteration cycles

Conditions

This is an on-site role based in our NYC or SF office, designed for builders who thrive on speed, iteration, and meaningful impact.

For this role, the expected base salary range is $230,000 to $325,000. Profound’s total compensation package is designed to be competitive and includes base salary, equity, and a full range of benefits and perks. Final compensation will depend on factors such as your skills, experience, qualifications, and location, and will be determined during the interview process. Our recruiting team will share more details about the full compensation package and benefits as you move through hiring.

LI-PRO

Note: All official communication from Profound will come from a @tryprofound.com email address. If you're contacted by anyone using a different domain, please disregard and report it as spam.