AI Researcher & ML Engineer | Remote (US) + Equity

Vivo HealthStaff Inc.; San Juan (PR); Hybrid; USD 175,000 - 300,000; Full time

Benefits offered by this job

Health benefits

Flexible PTO

Job summary

Vivo HealthStaff Inc. seeks an AI Researcher / Machine Learning Engineer to bridge frontier AI with real-world deployments. You will define roadmaps, set engineering bar, and take ML systems from concept to scalable production.

You will ship agentic components, evaluation, and robust pipelines to move models into live environments. The role emphasizes hands-on experimentation, fast iteration, and collaboration with product teams.

Qualifications

• 1–6 years of software or ML eng. experience.
• Experience shipping ML systems from concept to production.
• Proficiency in Python and Elixir.
• Top-tier AI tool user with curiosity.
• Ownership mindset and clear communication.
• Able to move fast and learn new stacks.

Responsibilities

• Design the agentic layer for scalable AI systems.
• Implement self-checking, reflection, and verification before results reach humans or downstream systems.
• Build systems to measure model performance across stores and refine models.
• Orchestrate agentic workflows and retrieval augmented generation.
• Productionize prompt pipelines, retrieval, and light fine-tuning to impact metrics.
• Stand up low-latency inference paths with observability.
• Own vision tooling, testing infra, and test models on edge and cloud.

Skills

Python

Elixir

Production ML

Ownership mindset

Communication

Fast learner

Education

Master's in Computer Science

PhD in Computer Science/Physics/Applied Mathematics/AI

Tools

LangGraph

Job description

Vivo HealthStaff Inc. seeks an AI Researcher / Machine Learning Engineer to bridge frontier AI with real-world deployments. You will define roadmaps, set engineering bar, and take ML systems from concept to scalable production.

You will ship agentic components, evaluation, and robust pipelines to move models into live environments. The role emphasizes hands-on experimentation, fast iteration, and collaboration with product teams.