Post-Training AI Engineer - End-to-End Production (On-site)

Profound; New York (NY); On-site; USD 230,000 - 325,000; Full time

Job summary

Profound is seeking a Post-Training Engineer to transform proprietary answer-engine data into high‑quality training datasets and ML pipelines. You will own end-to-end work from dataset to reward design, training runs, and deployment, ensuring the model delivers accurate, production-grade results to Fortune 500 customers in a fast-paced startup environment.

You’ll work with Python, PyTorch, and RL methods, collaborating across teams to define evaluation metrics and ship improvements that matter

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 into high‑quality training datasets.
• Build and iterate on SFT, preference optimization, and RL pipelines.
• Design reward signals that reflect what customers actually value.
• Train models for tool use and long‑horizon agentic workflows with the teams.
• Work with Evals to define what “better” means for each model.
• Prototype new methods on compute/data infrastructure and take to production.
• Ship what you train and monitor performance in production, feeding back into training cycles.

Skills

Python

PyTorch

JAX

SFT

RLHF

DPO

GRPO

RL methods

End-to-end ownership

Production deployment

Startup pace

Model evaluation

Tools

Kubernetes

DeepSpeed

Ray

SLURM

Job description

Profound is seeking a Post-Training Engineer to transform proprietary answer-engine data into high‑quality training datasets and ML pipelines. You will own end-to-end work from dataset to reward design, training runs, and deployment, ensuring the model delivers accurate, production-grade results to Fortune 500 customers in a fast-paced startup environment.

You’ll work with Python, PyTorch, and RL methods, collaborating across teams to define evaluation metrics and ship improvements that matter