Software Engineer (Frontend) at Latent AI

San Francisco, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years)Experience Level

Full TimeJob Type

UnknownVisa

Healthcare, AIIndustries

Requirements

Deep expertise in React and TypeScript
Proven experience architecting large, scalable frontend applications (not just marketing sites)
High standards for code quality and user experience
Domain curiosity, with enthusiasm for learning new technologies and complex clinical domains (e.g., Prior Authorizations)
Bias for action, willing to jump into tasks without waiting for others
High proficiency with tools, including fluent command line usage and keyboard shortcuts
Excitement about working five days per week in the San Francisco office
Healthcare experience (bonus): Familiarity with building interfaces that interact with EHR systems or manage complex clinical data

Responsibilities

Architect a flexible and capable React ecosystem designed to host complex, mission-critical healthcare widgets and user-facing AI tools
Focus on high-level UI/UX to help providers and operational staff effortlessly navigate clinical data and AI-surfaced insights for workflows like Prior Authorizations and Appeals
Design and implement interfaces that are critical to workflows involving Patient Interactions, ensuring clarity and speed are prioritized
Work closely with Full Stack and Backend Engineers to define interfaces and APIs, ensuring the frontend architecture supports rapid iteration and delivery of product specs

Skills

Key technologies and capabilities for this role

ReactUI/UXCommand LineFrontend ArchitectureAPI Integration

Questions & Answers

Common questions about this position

What is the salary range for the Frontend Engineer position?

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

Is this a remote position or does it require office work?

This is an onsite position requiring 5 days in-office in San Francisco, CA.

What are the key technical skills required for this role?

The role requires deep expertise in React and TypeScript, proven experience architecting large scalable frontend applications, and tool proficiency including command line and keyboard shortcuts.

What is the company culture like for engineers?

The culture is high-energy and in-office, emphasizing high standards for code quality and user experience, domain curiosity, and a bias for action.

What makes a strong candidate for this Frontend Engineer role?

Strong candidates are excited to translate complex backend and AI into intuitive UIs, have experience with healthcare domains like Prior Authorizations as a bonus, and thrive in a collaborative, fast-paced environment.

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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