Post-Training ML Engineer — Production AI, Equity

Profound; San Francisco (CA); On-site; USD 230,000 - 325,000; Full time

Job summary

Profound is seeking an experienced Post-Training Engineer to turn our answer-engine data into higher-quality training datasets and production-ready models. You will own end-to-end training workflows, including SFT, RLHF, and design of reward signals, collaborating with teams building AI Marketer and analytics products.

You will drive evaluation, set measurable targets, and push experimental ideas through to production in a fast-paced startup environment with rapid iteration cycles.

Qualifications

• 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.

Responsibilities

• 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

Skills

Python

PyTorch

JAX

Job description

Profound is seeking an experienced Post-Training Engineer to turn our answer-engine data into higher-quality training datasets and production-ready models. You will own end-to-end training workflows, including SFT, RLHF, and design of reward signals, collaborating with teams building AI Marketer and analytics products.

You will drive evaluation, set measurable targets, and push experimental ideas through to production in a fast-paced startup environment with rapid iteration cycles.