Site Reliability Engineer at Latent AI

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Healthcare, Artificial IntelligenceIndustries

Requirements

Highly proficient with command line and keyboard shortcuts
Proven track record of owning and scaling complex, mission-critical systems, including 500+ machine deployments
Deep expertise in Kubernetes and Helm for managing deployment, scaling, and operational health
Deep, demonstrable experience with Terraform for Infrastructure as Code (IaC)
Hands-on experience optimizing deployment pipelines for TypeScript and Python/ML applications
Experience with PostgreSQL, Redis, and Kafka
Proven ability to architect and maintain complex, distributed systems with high-availability requirements
Thrives in a high-energy, in-office culture with intensity, technical mastery, ownership, automation drive, and problem-solving mindset
Excitement about working five days per week in San Francisco office

Responsibilities

Design, implement, and maintain the production environment
Own containerized infrastructure using Kubernetes and Helm
Optimize and streamline TypeScript and Python/ML deployment pipelines for high-velocity releases and reliability
Support Developer Experience (DevX) to streamline developer workflows, enhance tool proficiency, and improve CI/CD systems
Manage and maintain infrastructure definitions using Terraform
Own the entire production environment and improve the development experience

Skills

Key technologies and capabilities for this role

KubernetesHelmPythonTypeScriptCI/CDIaCCommand LineAutomation

Questions & Answers

Common questions about this position

What is the salary range for the Site Reliability Engineer position?

The pay scale for this role is $165,000-250,000.

Is this a remote or in-office position?

This is a fully onsite position requiring five days per week in the San Francisco office.

What technical skills are required for this SRE role?

Required skills include deep experience with Kubernetes, Helm, and Terraform, proven ability to scale complex distributed systems, and hands-on optimization of deployment pipelines for TypeScript and Python/ML, along with PostgreSQL, Redis, and Kafka.

What is the company culture like at Latent AI?

The culture is high-energy and in-office, where team members hold themselves and others to high standards, thrive on intensity, and are all in it to win it.

What makes a strong candidate for this SRE position?

Strong candidates demonstrate tool proficiency like fluent command line use, ownership of complex systems with a track record of scaling 500+ machine deployments, a drive for automation and operational excellence, and proactive problem-solving.

Latent AI

Optimizes AI for edge computing applications

About Latent AI

Latent AI focuses on enhancing artificial intelligence for edge computing, which involves processing data close to where it is generated, like on smartphones and IoT devices. Their main product is an edge MLOps workflow that automates and simplifies the deployment of AI models on these devices, ensuring they are efficient, secure, and require minimal maintenance. This company stands out from competitors by specifically targeting the edge computing market and providing a software development kit (SDK) called Latent LEIP, which aids developers in creating and deploying AI models. The goal of Latent AI is to make AI more accessible and efficient for applications that need real-time data processing, such as in autonomous vehicles and smart cities.

Menlo Park, CaliforniaHeadquarters

2018Year Founded

$21.9MTotal Funding

SERIES_ACompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine LearningIndustries

51-200Employees

Risks

Competition from tech giants like Google and Amazon threatens Latent AI's market share.

Rapid AI advancements may outpace Latent AI's current capabilities, requiring continuous innovation.

Dependency on key partnerships, like Booz Allen, poses risks if relationships change.

Differentiation

Latent AI specializes in optimizing AI for edge computing applications.

Their edge MLOps workflow ensures efficient, secure AI model deployment on edge devices.

Latent AI's SDK, Latent LEIP, streamlines AI model creation and deployment for developers.

Upsides

Growing interest in federated learning enhances privacy and efficiency for edge AI.

The rise of TinyML aligns with Latent AI's focus on resource-constrained edge devices.

5G technology adoption boosts data transmission speed for Latent AI's solutions.

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