Software Engineer (Full-Stack) at Latent AI

San Francisco, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Healthcare, Health TechIndustries

Requirements

Generalist mindset, thriving on owning projects end-to-end
High standards, proficiency with keyboard shortcuts, and passion for automation to increase leverage
Experience in both frontend and backend development
Product focus: ability to work with customers, define product specs, and iterate quickly
Bias for action: willingness to jump into tasks without waiting for others
Deep proficiency with TypeScript and React
Strong experience with Redis, PostgreSQL, Temporal, and Kafka
Proven system design skills to manage complexity with simple abstractions
Excitement about working five days per week in San Francisco office

Responsibilities

Drive product initiatives and manage technical delivery across the entire application
Design, build, and deploy new features from API development to user interface implementation (end-to-end delivery)
Architect and manage solution complexity, define standards, and create simple abstractions
Dive deep into Prior Authorizations, Patient Interactions, and EHR Integrations for healthcare improvements
Integrate and utilize core Clinical AI Models for user-facing features

Skills

Key technologies and capabilities for this role

Full-Stack DevelopmentFrontend DevelopmentBackend DevelopmentAPI DevelopmentDatabaseUser InterfaceSystem ArchitectureEHR IntegrationAI Implementation

Questions & Answers

Common questions about this position

What is the salary range for this Full-Stack Engineer position?

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

Is this position remote or onsite, and what's the location?

This is an onsite position requiring 5 days per week in the San Francisco office.

What technical skills are required for this role?

Required skills include deep proficiency in TypeScript and React, strong experience with Redis, PostgreSQL, Temporal, and Kafka, plus proven system design abilities.

What is the company culture like for engineers?

It's a high-energy environment for builders who thrive on total ownership, product focus, bias for action, and working end-to-end from database to pixels.

What makes a strong candidate for this Full-Stack Engineer role?

Strong candidates are generalists who hold high standards, love automation and keyboard shortcuts, have extensive frontend and backend experience, and are excited about 5 days in-office in San Francisco.

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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