Applied AI Researcher (Optimization & Domain Models)

This is a remote position.

Role Type: Research-to-Production Specialist Focus: Domain-Specific Models, Optimization & Safety Reports to: Chief AI Evangelist & Product Head

Role Summary

The Applied AI Researcher (Optimization & Domain Models) is responsible for adapting and optimizing AI models for micro-industry-specific problems. This role bridges theoretical rigor and real-world deployment, ensuring models are not only accurate but safe, explainable, and production-ready.

You will own problem-specific reasoning models, vertical LLM tuning, anomaly detection, and quality models that power defensible SaaS offerings.

Key Responsibilities

Domain Model Development

• 
  Design problem-specific reasoning and optimization models.
• 
  Fine-tune vertical LLMs for industry-specific language, workflows, and constraints.
• 
  Build anomaly detection, prediction, and quality inspection models.
• 
  Adapt foundation models to operate under domain rules, policies, and regulations.

Evaluation, Guardrails & Safety

• 
  Own evaluation loops (offline, online, human-in-the-loop).
• 
  Design guardrails for hallucination control, bias mitigation, and policy compliance.
• 
  Implement safety tooling for enterprise-grade AI deployments.
• 
  Define success metrics tied to business and operational outcomes.

Research to Production

• 
  Convert research prototypes into deployable, scalable micro-industry models.
• 
  Partner with engineers to integrate models into agents and SaaS workflows.
• 
  Document model behavior, assumptions, and failure modes.
• 
  Create repeatable model adaptation playbooks.

IP & Thought Leadership

• 
  Contribute to proprietary model architectures and training strategies.
• 
  Publish internal whitepapers and external POVs where appropriate.
• 
  Support GTM narratives with credible technical depth.

Required Qualifications

• 
  Desirable PhD in AI, ML, Applied Mathematics, Operations Research, or related field.
• 
  Strong background in optimization, probabilistic modeling, or deep learning.
• 
  Experience fine-tuning LLMs or training domain-specific models.
• 
  Hands-on experience with Python, PyTorch, TensorFlow, or JAX.

Preferred Qualifications

• 
  Experience with enterprise or regulated domains (healthcare, finance, telecom).
• 
  Familiarity with reinforcement learning or constrained optimization.
• 
  Exposure to safety, alignment, or AI governance frameworks.

Success Metrics

• 
  Deliver 1 domain-tuned model per quarter.
• 
  Demonstrate measurable performance lift vs baseline models.
• 
  Deploy models into at least 2 production workflows.
• 
  Reduce inference errors or quality issues by 25%.