Join Profound as a Post-Training Engineer to enhance AI models for Fortune 500 marketing teams in a fast-paced environment.
Posted by employer 1 week ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
What you'll build
Must have
Nice to have
AI in the day-to-day
Profound’s AI Marketer uses data to automate reporting, draft content, and improve model performance based on real customer interactions.
Not disclosed in this posting: years of experience, visa sponsorship.
Benefits
Joblaze summary
In the role of Post Training Engineer at Profound, the individual will focus on transforming proprietary answer-engine data into high-quality training datasets and developing models that enhance AI performance for marketing teams. Key skills include proficiency in Python and PyTorch, as well as hands-on experience with reinforcement learning methods and distributed training. This position is suited for someone with a strong software engineering background and a track record of managing projects from inception to deployment. Profound operates in a fast-paced environment, emphasizing rapid iteration and impactful results.
Joblaze insights
Quick facts
From the original posting
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.
What you’ll do
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
Who you are
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
Location
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.
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