Post-Training Engineer (AI)

Location: On-site in the New York City or San Francisco office, United States

Base salary: $230,000–$325,000 per year, plus equity and benefits.

Company

hirify.global is an AI marketing platform used by Fortune 500 companies and other technology companies to analyze AI visibility and automate marketing work with agentic tools.

What you will do

• Turn prompts, responses, citations, and query fanouts into high-quality training datasets.
• Build and iterate on SFT, preference optimization, and RL pipelines.
• Design reward models, LLM judges, and verifiable rewards that reflect customer value.
• Train models for tool use and long-horizon agentic workflows with the AI Marketer and analytics teams.
• Define model quality with the Evals team and prototype methods on compute and data infrastructure.
• Deploy trained models, own production performance, and use regressions and customer behavior to guide future training cycles.

Requirements

• Exceptional software engineering skills in Python and PyTorch; JAX is a plus.
• Hands-on post-training experience with SFT, RLHF, DPO, GRPO, or similar RL methods.
• Experience with distributed training using technologies such as FSDP, DeepSpeed, Ray, Kubernetes, or Slurm.
• Strong evaluation judgment, including the ability to identify misleading metrics.
• Track record of owning work end to end, from early experiments to shipped models.
• Strong understanding of model behavior and ongoing engagement with current research.
• Comfort operating in a fast-paced startup environment with rapid iteration cycles.

Nice to have

• Experience with agentic or tool-use training, synthetic data, or reward model design.
• Experience with retrieval or search-grounded models.
• Publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, or EMNLP.

Culture & Benefits

• On-site work in the NYC or SF office for builders who thrive on speed, iteration, and meaningful impact.
• Competitive total compensation including base salary, equity, and a full range of benefits and perks.
• Final compensation depends on skills, experience, qualifications, and location.