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New York, New York, United States
Onsite
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 Post-Training Engineer, you will turn Profound’s proprietary answer-engine data into models that are measurably better at the work our customers rely on: analyzing AI visibility, attributing citations, classifying sentiment, verifying claims, using tools, and running long-horizon agentic workflows. Most post-training is measured against benchmarks. Ours is measured by whether a Fortune 500 marketing team gets the right answer. You’ll own the full loop, from dataset to reward design to training run to deployment, then watch how your model performs with real customers and feed that into the next cycle.
What you’ll do
Turn Profound’s answer-engine data (prompts, responses, citations, query fanouts) into high-quality training datasets
Build and iterate on SFT, preference optimization, and RL pipelines
Design reward signals that reflect what customers actually value: reward models, LLM judges, and verifiable rewards
Train models for tool use and long-horizon agentic workflows with the teams building AI Marketer and our analytics products
Work with the Evals team to define what “better” means for each model, and hold every training run to it
Prototype new methods on our compute and data infrastructure, and take the ones that work to production
Ship what you train, own its performance in production, and turn regressions and customer behavior into the next training cycle
Who you are
Exceptional software engineering skills in Python and PyTorch (JAX a plus)
Hands-on post-training experience (SFT, RLHF, DPO, GRPO, or similar RL methods)
Experience with distributed training (e.g. FSDP, DeepSpeed, Ray; Kubernetes or Slurm)
Strong instincts for evaluation, and the ability to notice when a metric is misleading
A track record of owning work end to end, from early experiment to shipped model
Strong opinions about how models behave, and you keep up with current research
You’d rather see your model in production than in a paper
Comfortable operating in a fast-paced startup environment with rapid iteration cycles
Nice to have (optional):
Experience with agentic or tool-use training, synthetic data, or reward model design
Experience with retrieval or search-grounded models
Publications at venues like NeurIPS, ICML, ICLR, MLSys, ACL, or EMNLP
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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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.
