Remote Senior AI Platform Engineer: ML Infra Leader

Storm3; California; Remote; USD 180,000 - 220,000; Full time

Job summary

Storm3 is seeking a Senior AI Platform Engineer to lead the design and optimization of production-grade AI/ML systems. This remote role focuses on backend infrastructure, distributed systems, and microservices, with a strong emphasis on reliability and performance in healthtech environments.

You will collaborate across teams to accelerate model training, deployment, and monitoring, while mentoring peers and upholding best practices in ML infrastructure.

Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, or related field with 5+ years of experience.
• Strong expertise in large distributed ML systems, MLOps, and software design principles.
• Proficiency in Python, with knowledge of Java or Go a plus.
• Hands-on experience with Docker, Kubernetes, and microservices architecture.
• Familiarity with SQL, NoSQL, and big data frameworks like Spark.
• Experience with LLMs or GenAI is highly desirable.

Responsibilities

• Design, build, and optimize production-grade AI/ML systems focusing on backend infrastructure and microservices.
• Lead cross-functional projects, aligning scope, timelines, and dependencies to deliver impactful solutions.
• Collaborate with AI/ML engineers to streamline model training, deployment, and monitoring processes.
• Establish and enforce SLAs for latency, throughput, and system resources.
• Develop tools for model lifecycle management, monitoring, and performance tuning.
• Mentor team members and promote best practices in ML infrastructure.

Skills

Distributed ML systems

MLOps

Python programming

Software design principles

Leadership and mentoring

Education

Bachelor's or Master's in CS/Engineering

Tools

Docker

Kubernetes

Spark

SQL/NoSQL databases

Job description

Storm3 is seeking a Senior AI Platform Engineer to lead the design and optimization of production-grade AI/ML systems. This remote role focuses on backend infrastructure, distributed systems, and microservices, with a strong emphasis on reliability and performance in healthtech environments.

You will collaborate across teams to accelerate model training, deployment, and monitoring, while mentoring peers and upholding best practices in ML infrastructure.