Senior System Engineer

Matrice AI; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

Matrice AI is seeking a Principal Systems Engineer to architect and operate the distributed infrastructure behind video processing, ML inference, and real-time data workloads. You will focus on reliability, performance, automation, and recovery while partnering with ML engineers and guiding engineering practices team-wide.

Responsibilities include designing scalable cloud infrastructure, optimizing video and streaming workloads, building observability, and leading CI/CD and IaC initiatives.

Qualifications

• Requires at least three years in systems engineering, DevOps, or SRE building distributed systems.
• Experience with Go or another systems language and MongoDB.
• Strong cloud knowledge across AWS, GCP, or Azure.
• Advanced Docker and Kubernetes skills, plus CI/CD pipelines and IaC with Terraform or CloudFormation.
• Deep understanding of distributed systems, microservices, and system design.
• Strong Linux, networking, security, performance, and observability skills; able to lead technical discussions and mentor others.
• Bachelor's degree in computer science, engineering, or related field, or equivalent experience.

Responsibilities

• Design and maintain scalable, fault-tolerant systems and cloud infrastructure.
• Optimize infrastructure for video processing, inference pipelines, and streaming data workloads.
• Build monitoring, observability, and alerting for reliable operations.
• Lead infrastructure automation, CI/CD, and infrastructure as code initiatives.
• Develop backup, disaster recovery, and high availability solutions.
• Investigate complex incidents, identify root causes, implement preventive improvements, and mentor engineers.

Skills

Go

Kubernetes

Cloud platforms

CI/CD

Terraform

CloudFormation

MongoDB

Docker

Linux

Distributed systems

Education

Bachelor's degree in CS/Engineering

Tools

Terraform

CloudFormation

MongoDB

Docker

Kubernetes

AWS

GCP

Azure

Job description

Role overview

Architect and operate the distributed infrastructure behind systems that process video, machine learning inference, and real time data. The role covers reliability, performance, automation, and recovery, while partnering with ML engineers and guiding engineering practices across the team.

Responsibilities

• Design and maintain scalable, fault tolerant systems and cloud infrastructure.
• Optimize infrastructure for video processing, inference pipelines, and streaming data workloads.
• Build monitoring, observability, and alerting that support reliable operations.
• Lead infrastructure automation, CI/CD, and infrastructure as code initiatives.
• Develop backup, disaster recovery, and high availability solutions.
• Investigate complex incidents, identify root causes, implement preventive improvements, and mentor engineers.

Requirements

• At least three years in systems engineering, DevOps, or site reliability engineering, building and maintaining distributed systems.
• Experience with Go or another systems language and MongoDB or another NoSQL database.
• Strong cloud knowledge across compute, storage, networking, and managed services in AWS, GCP, or Azure.
• Advanced Docker and Kubernetes skills, plus experience building CI/CD pipelines and infrastructure as code with tools such as Terraform or CloudFormation.
• Deep understanding of distributed systems, microservices, and system design.
• Strong Linux, networking, security, performance, and observability skills; able to lead technical discussions and mentor others.
• Bachelor's degree in computer science, engineering, or a related field, or equivalent experience.

Nice to have

• Experience with video streaming, real time data processing, or machine learning infrastructure.

Benefits and work setup

The position is described as remote first, with flexible work arrangements, competitive compensation, and a learning and development budget for cloud certifications and system design training.