[Remote] Security Engineer: App Sec Lead at Latent AI

San Francisco, California, United States

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Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, HealthcareIndustries

Requirements

Experience creating, building, or scaling an application security program in a cloud-first organization
Primary coding language: Javascript (NodeJS/Typescript) and Python
Experience with threat modeling and architecture reviews
Experience working with engineering and technical leadership to build security processes like vulnerability management
Deep understanding of web and API-based security vulnerabilities

Responsibilities

Choosing the right App Sec tools for the environment to make code secure before it is shipped
Creating and maturing processes around vulnerability management, architecture reviews, pentesting, and threat modeling
Doing code reviews and bug fixing
Helping build and POC new secure ways of writing code (validation libraries, authentication/authorization practices, encryption SDKs)
Helping re-imagine permissioning and authorization for users of the Latent platform
Working alongside engineers to balance business requirements with security controls
Creating a mature pentesting and/or bug bounty program
Bringing security checks and tooling to developer workflows (AI-based IDEs, CI/CD, etc.)

Skills

Key technologies and capabilities for this role

Application SecurityVulnerability ManagementArchitecture ReviewsPentestingThreat ModelingCode ReviewsNodeJSTypescriptPython

Questions & Answers

Common questions about this position

Is this position remote or does it require office work?

The position offers a remote or in-office option in San Francisco or New York.

What programming languages are required for this role?

The primary coding languages are Javascript (NodeJS/Typescript) and Python.

What salary or compensation is offered for this position?

This information is not specified in the job description.

What experience makes a strong candidate for this App Sec Lead role?

Strong candidates have experience creating, building, or scaling a hands-on application security program in a cloud-first organization, along with skills in threat modeling, architecture reviews, and deep knowledge of web and API-based security vulnerabilities.

What does the company look for in a great application security engineer?

They seek someone excited about building a greenfield application security program from scratch, getting hands-on with the codebase in NodeJS/Typescript/Python, building processes with engineers, performing pentesting, and eventually helping hire the team.

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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