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Full-Time

Software Engineer

Developer Tools and Productivity

Confirmed live in the last 24 hours

Kumo

Kumo

51-200 employees

Generates and deploys predictive models using AI

Enterprise Software
AI & Machine Learning

Mid

Mountain View, CA, USA

Hybrid position requiring in-office presence.

Category
Backend Engineering
Full-Stack Engineering
Software Engineering
Required Skills
Chef
Bash
Kubernetes
Microsoft Azure
Python
JavaScript
Java
Docker
AWS
Terraform
Ansible
Google Cloud Platform
Requirements
  • BS (preferred MS, PhD.) in Computer Science.
  • Must Have: B2B Saas experience, architecting experience in building a large-scale distributed system at scale.
  • 3+ years of experience writing production code in Java, Javascript, C++, or Python (NO NEW GRADS)
  • Experience with productionizing cloud applications, including Docker and Kubernetes, CI/CD and advanced packaging, versioning, and deployment strategies, containers and serverless architecture, online/offline feature stores, model performance monitoring
  • Familiarity with popular MLOps tooling from cloud vendors like GCP (Vertex AI), AWS (SageMaker) or Azure Machine Learning and MLFlow, Kubeflow, etc.
  • Proficiency with general full-stack application development, such as defining data models, building abstractions for business logic, and developing customer-facing Web Front Ends or public APIs/SDKs for the application.
  • Experience with Infrastructure-as-code development (e.g., Terraform, Cloud Formation, Ansible, Chef, Bash scripting, etc.)
  • Core understanding of data modeling and fundamentals of data engineering (e.g. integrations/connectors, pipelines, ETL/ELT processes)
Responsibilities
  • Build and extend components of the core Kumo infrastructure
  • Willingness to respond and be a key participant in our incident management process and develop tools for better Root Cause Analysis and reduction in MTTR (mean time to respond)
  • Build and automate CI-CD pipelines, and release tooling to support continuous delivery and true zero-downtime deployments across different cloud providers using the latest cloud-native technologies.
  • Build the Kumo ML Ops platform, which will be able to data drift, track model versions, report on production model performance, alert the team of any anomalous model behavior, and run programmatic A/B tests on production models.
  • You will work on advanced tools developed for the world’s leading cloud-native machine learning engine that uses graph deep learning technology.

Kumo.ai specializes in creating and deploying predictive models that help organizations make accurate forecasts for critical tasks. Their platform uses Graph Neural Networks to analyze raw relational data, which means it can generate predictions without needing extensive manual data preparation. This approach leads to better accuracy and efficiency while also reducing infrastructure costs by eliminating the need for complex data processing systems. Kumo.ai's platform supports the entire Machine Learning lifecycle, from data preparation to model deployment, and is designed to deliver quick returns on investment for various applications, including customer retention and fraud detection. Unlike many competitors, Kumo.ai offers both Software as a Service and Private Cloud options, making it adaptable for businesses of all sizes. The goal of Kumo.ai is to provide reliable predictive insights that enhance decision-making and operational efficiency for its clients.

Company Stage

Series B

Total Funding

$54.5M

Headquarters

Mountain View, California

Founded

2021

Growth & Insights
Headcount

6 month growth

24%

1 year growth

36%

2 year growth

97%
Simplify Jobs

Simplify's Take

What believers are saying

  • The partnership with Snowflake enhances Kumo.ai's scalability and ease of use, making it more attractive to data scientists.
  • The recent $18 million Series B funding led by Sequoia Capital provides financial stability and resources for further innovation.
  • Kumo.ai's SQL-like Predictive Querying Language simplifies model creation, enabling rapid deployment and broader adoption.

What critics are saying

  • The competitive landscape in predictive AI is intense, with major players like Google and OpenAI posing significant threats.
  • Reliance on partnerships, such as with Snowflake, could limit Kumo.ai's flexibility and independence.

What makes Kumo unique

  • Kumo.ai leverages Graph Neural Networks to eliminate the need for manual feature engineering, setting it apart from traditional ML platforms.
  • The platform's end-to-end capabilities, from data preparation to deployment, streamline the entire ML lifecycle, unlike competitors that require multiple tools.
  • Kumo.ai's high availability SLAs and SOC2 compliance offer robust security and reliability, appealing to enterprise clients.

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