Data • New York, New York • San Francisco, California • In-office
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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
LocationThis 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 email address. If you're contacted by anyone using a different domain, please disregard and report it as spam.
