Senior AI Engineer, Personalization & Recommendations (Remote)

Meridian Street Capital, LLC; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Remote work

Transparent pay

Health benefits

401(k) plan

Generous time off

Parental leave

EAP

Home office stipend

Wellness stipend

Job summary

Rula is a remote-first company hiring in most U.S. states (Hawaii excluded). We are seeking a senior AI/ML leader to own the applied AI/ML stack powering patient-provider matching, clinical workflows, and engagement.

The role centers on search ranking, personalization, and building scalable AI foundations for real-world care. You will develop and fine‑tune models (Learning‑to‑Rank, embeddings, LLMs), design product-facing AI architectures, and guide ML infrastructure across the stack to improve

Qualifications

• 10+ years in software engineering with distributed systems experience.
• 5+ years building and deploying production ML applications and systems.
• 5+ years in search/ranking/recommendation engines at scale.
• 3+ years with MLOps, data pipelines, evaluation systems.

Responsibilities

• Own applied AI/ML solutions powering patient matching, workflows, and engagement.
• Develop production-grade AI systems and scalable ML infrastructure.
• Tune models (Learning-to-Rank, embeddings, recommendations) and build AI architectures.
• Set technical strategy for AI/ML in healthcare with safety and reliability.

Skills

Distributed systems design

Search/recommendation engines

Python programming

LLM integration patterns

Elasticsearch/OpenSearch

Vector databases

MLOps pipelines

Tools

Pinecone

Weaviate

FAISS

Milvus

Job description

Rula is a remote-first company hiring in most U.S. states (Hawaii excluded). We are seeking a senior AI/ML leader to own the applied AI/ML stack powering patient-provider matching, clinical workflows, and engagement.

The role centers on search ranking, personalization, and building scalable AI foundations for real-world care. You will develop and fine‑tune models (Learning‑to‑Rank, embeddings, LLMs), design product-facing AI architectures, and guide ML infrastructure across the stack to improve