AI Systems Builder (Impact-Driven) [33169]

Stealth Startup
Stanford, United States of America
9 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Junior

Job location

Stanford, United States of America

Tech stack

Artificial Intelligence
Distributed Computing Environment
Python
PyTorch
Large Language Models
Multi-Agent Systems
Model Validation
HuggingFace
Virtual Agents

Job description

We're hiring an Applied AI Engineer to design, fine-tune, and deploy production-grade AI models powering next-generation AI agents in financial workflows.

This role sits at the intersection of research and real-world deployment - taking models from experimentation to high-stakes production systems.

What You'll Do

Model Development

  • Fine-tune LLMs and multimodal models for domain-specific use cases
  • Design post-training workflows (instruction tuning, RLHF, evaluation loops)
  • Optimize training pipelines using open-source frameworks
  • Run ablation studies and hyperparameter tuning

AI Agent Systems

  • Build AI agents capable of executing multi-step workflows
  • Integrate models into production environments
  • Improve robustness, accuracy, and reliability
  • Develop evaluation frameworks for edge cases

Data & Collaboration

  • Work with proprietary datasets
  • Build labeling and data-quality pipelines
  • Partner with product and engineering teams globally

Requirements

  • 1-4 years hands-on ML/AI training experience
  • Strong Python + PyTorch
  • Experience with Hugging Face ecosystem
  • Experience deploying models to production
  • Understanding of model evaluation trade-offs

Bonus: RLHF, model distillation, RAG, distributed training, GPU optimization, multi-agent systems.

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