Software Engineer - Infrastructure

Mountain View, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, AI & Machine Learning, Data Management, Cloud ComputingIndustries

Requirements

Candidates must possess a Bachelor's degree in Computer Science, with a Master's or PhD preferred, and have over 5 years of software development experience. A strong foundation in distributed systems design principles and proficiency in languages such as Python, Java, or C++ are essential. Experience with cloud distributed storage, databases, file systems (AWS, Azure), scaling microservices, ML frameworks like PyTorch or TensorFlow, and contributions to open-source projects in distributed systems or data processing are highly valued. Understanding of ML fundamentals, particularly in enterprise applications, and experience designing systems that handle failure modes and restarts are also beneficial.

Responsibilities

The Infrastructure Engineer will design and implement the core architecture for distributed training and inference systems capable of handling enterprise-scale data. Responsibilities include crafting integration points between data warehouses, ML processing engines, and proprietary Graph Neural Network technology, and building sophisticated orchestration systems for optimizing computational resources. The role also involves developing clean APIs and abstractions for decoupled system components, creating scalable, cloud-native infrastructure, and collaborating with customers on real-world deployments.

Skills

Distributed Systems

Scalability

Cloud-native infrastructure

Data Warehouses

Machine Learning Workflows

Graph Neural Networks

API Design

Orchestration Systems

Enterprise Security

Performance Optimization

Kumo

Generates and deploys predictive models

About Kumo

Kumo.ai specializes in creating and implementing accurate predictive models for organizations that need reliable forecasts for critical operations. Their platform uses Graph Neural Networks to analyze raw relational data, which removes the need for manual data preparation and enhances prediction accuracy and efficiency. Unlike many competitors, Kumo.ai's platform streamlines the entire Machine Learning lifecycle, from data preparation to model deployment, while also optimizing costs by eliminating unnecessary infrastructure. The company aims to provide a quick return on investment for its clients, which range from small businesses to large enterprises, by offering flexible deployment options through Software as a Service (SaaS) and Private Cloud models. Kumo.ai is built by experienced professionals from top tech companies and has already gained the trust of leading organizations globally.

Mountain View, CaliforniaHeadquarters

2021Year Founded

$35.5MTotal Funding

SERIES_BCompany Stage

Fintech, AI & Machine LearningIndustries

51-200Employees

Benefits

Stock Options

Medical Insurance

Dental Insurance

Risks

Increased competition from Databricks' Marketplace may divert potential customers.

The rise of multimodal AI could overshadow Kumo's current offerings.

Rapid AI advancements by tech giants may set new industry standards Kumo must meet.

Differentiation

Kumo.AI uses Graph Neural Networks for predictive modeling, eliminating manual feature engineering.

The platform offers a SQL-like Predictive Querying Language for rapid AI model creation.

Kumo.AI integrates with Snowflake's Native App Framework, enhancing model performance and scalability.

Upsides

Kumo's $18M Series B funding will expand its platform and market reach.

Integration with Snowpark Container Services enhances deep learning capabilities within Snowflake Data Cloud.

Kumo's platform supports both SaaS and Private Cloud models, offering client flexibility.

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