Full-Time
Software Engineer
Cloud Engineering, Kubernetes
Confirmed live in the last 24 hours
Kumo
51-200 employees
Generates and deploys predictive models using AI
No salary listed
Senior
Mountain View, CA, USA
This is a hybrid position, which typically requires some in-office days.
- 5-7+ years of experience managing large-scale Kubernetes clusters (EKS, GKE, AKS, or OpenSource) in production.
- Deep expertise in Kubernetes internals, including controllers, operators, scheduling, networking (CNI), and security policies.
- 5-7+ years of experience building cloud-native Kubernetes-based infrastructure across AWS, Azure, and GCP.
- 5-7+ years of experience building Kubernetes service meshes (Istio/Envoy, Traefik), networking policies (Calico/Tigera), and distributed ingress/egress control.
- Proven experience in optimizing, scaling, and maintaining Kubernetes clusters across multi-cloud environments, ensuring high availability and performance.
- 5-7+ years of experience writing production-grade controllers and operators in Python, Go, or Rust to extend Kubernetes functionality.
- Hands-on experience with Terraform, CloudFormation, Ansible, BASH and Make scripting to automate Kubernetes cluster provisioning and management.
- Expertise in building and operating large-scale distributed systems for cloud-native B2B SaaS applications running on Kubernetes.
- Deep expertise in building of container orchestration, workload scheduling, and runtime optimizations using Kubernetes, Argo or Flux.
- BS/MS in Computer Science or a related field (PhD preferred)
- Design, build, and scale Kubernetes-based infrastructure to support Kumo’s multi-cloud AI platform, ensuring high availability, resilience, and performance.
- Architect and optimize large-scale Kubernetes clusters, improving scheduling, networking (CNI), and workload orchestration for production environments.
- Develop and extend Kubernetes controllers and operators to automate cluster management, lifecycle operations, and scaling strategies.
- Enhance observability, diagnostics, and monitoring by building tools for real-time cluster health tracking, alerting, and performance tuning.
- Lead efforts to automate fleet management, optimizing node pools, autoscaling, and multi-cluster deployments across AWS, GCP, and Azure.
- Define and implement Kubernetes security policies, RBAC models, and best practices to ensure compliance and platform integrity.
- Collaborate with ML engineers and platform teams to optimize Kubernetes for machine learning workloads, ensuring seamless resource allocation for AI/ML models.
- Drive commit-to-production automation, cloud connectivity, and deployment orchestration, ensuring seamless application rollouts, zero-downtime upgrades, and global infrastructure reliability.
- Proficiency with cloud platforms such as AWS, GCP, or Azure.
- Familiarity with chaos engineering tools and practices for testing system resilience.
- Strong understanding of security best practices and compliance standards (GDPR, SOC2, ISO27001, vulnerability assessments, GRC, risk management).
- Contributions to open-source projects, particularly in the Kubernetes or cloud-native ecosystem.
- Expertise in Docker, Kubernetes, Jenkins, Flux, Argo, and Terraform in a Linux environment.
- Hands-on experience with monitoring and observability tools such as Prometheus and Grafana.
- Ability to develop customer-facing web frontends or public APIs/SDKs for platform services.
Kumo.ai creates predictive models that help organizations make accurate forecasts for important tasks like customer retention and fraud detection. Their platform uses Graph Neural Networks to analyze raw data, which improves accuracy and reduces the need for complex data processing. Kumo.ai supports the entire Machine Learning lifecycle and offers both Software as a Service (SaaS) and Private Cloud options, making it suitable for various clients. The company's goal is to provide reliable predictive solutions that deliver quick returns on investment.
Company Size
51-200
Company Stage
Series B
Total Funding
$36.5M
Headquarters
Mountain View, California
Founded
2021
Simplify's Take
What believers are saying
- 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.
What critics are saying
- 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.
What makes Kumo unique
- 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.
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Stock Options
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Growth & Insights and Company News
6 month growth
↓ -2%1 year growth
↓ -1%2 year growth
↑ 1%Data scientists can build highly accurate predictive AI models faster with Kumo AISAN FRANCISCO, June 4, 2024 /PRNewswire/ -- Today at Snowflake Data Cloud Summit 2024 , Kumo AI , a leader in predictive AI, announced it has launched Kumo as a Snowflake Native App in Snowflake Marketplace in private preview. Kumo helps data scientists build highly accurate machine learning (ML) models that better predict user and customer behavior by combining graph learning over enterprise data with generative AI models trained on public data. Kumo's Snowflake Native App is available through Snowflake Marketplace, can be paid for with Snowflake Capacity commitment for eligible customers, and is built using the Snowflake Native App Framework, enabling data scientists to develop predictive models faster than ever before."We understand how difficult building high-performing ML models can be. Our partnership with Snowflake simplifies the process and eliminates many of the typical hurdles that data scientists experience," said Vanja Josifovski, co-founder and CEO of Kumo AI. "By coupling Snowflake's well-known scalability, speed, and reliability with Kumo's specialized AI to identify and maximize the signal hidden in Snowflake data, we can produce highly accurate predictive ML models that are based on the entirety of your data including multiple relational tables, text, and images. Joint customers can unlock the full potential of their Snowflake data to fuel data-driven decisions and make granular predictions such as personalized recommendations."Businesses are trying to maximize revenue-impacting KPIs like conversion, cart size, and retention rates using insights uncovered from historic data, but struggle with efficient approaches and significant model improvements
In the cutthroat world of artificial intelligence, tech behemoths are betting big on a new frontier: multimodal AI. As the shine of text-based chatbots dims, companies are gambling that the future belongs to AI assistants capable of seeing, hearing and conversing with users more naturally and intuitively. The battle for AI dominance has taken on a new dimension, with “multimodal” emerging as the latest buzzword. “The usefulness of multimodal AI comes from its ability to simultaneously process and analyze diverse types of data, such as text, images, audio and video,” ComplyControl Chief AI Officer Mikhail Dunaev told PYMNTS
Credit: cono0430, ShutterstockAn open letter signed by a range of big and small tech companies has committed to responsible development of Artificial Intelligence (AI) at a time of increased public focus on the technology. Four of the the most high-profile signatories of the letter are Google, Microsoft, Meta and OpenAI, four of the most influential firms in global AI products – the latter is notable for being the creator of widely used AI platform ChatGPT. Due to continued development, AI has become increasingly utilised for business purposes, such as automation of general day-to-day tasks, risk management, anti-money laundering and fraud prevention
MOUNTAIN VIEW, Calif. , Nov. 16, 2023 /PRNewswire/ -- Kumo.AI , a Sequoia Capital-backed predictive AI company, announced the general availability of its Kumo.AI platform, enabling rapid creation and deployment of state-of-the-art AI models on private enterprise data. AI practitioners can now use Kumo.AI's intuitive SQL-like Predictive Querying Language to build multiple task-specific AI models in a single day. Founded in 2021 by Stanford University Professor (Jure Leskovec) and former Airbnb and LinkedIn executives (Vanja Josifovski, Hema Raghavan), Kumo.AI helps enterprises unlock customer-focused use cases, such as personalization, churn and LTV prediction, fraud detection, and forecasting.Kumo, cutting edge AI that can query the future (PRNewsfoto/Kumo.AI)While generative AI excels at natural language understanding and generation, it lacks the capability to perform more complex, enterprise-specific tasks, such as predicting customer behavior or detecting fraudulent transactions. Kumo.AI is the first platform that bridges this gap by applying deep representation learning, a technology behind the current AI revolution, to enterprise data in data warehouses
Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More. Kumo, a deep learning platform for relational data, announced today at Snowflake Summit 2023 its integration of deep learning capabilities directly into the Snowflake Data Cloud through Snowpark Container Services.Snowflake’s recently introduced Snowpark Container Services expands the functionality of Snowpark. This update allows organizations to run third-party software and full-stack applications within their Snowflake accounts.According to Snowflake, with this integration customers can maximize their data potential by using cutting-edge tools while maintaining data security and eliminating the need for data movement.Moreover, Snowpark Container Services includes GPU support, which gives data science and machine learning teams a way to accelerate development and bridge the gap between model deployment and consistent data security and governance throughout the AI/ML lifecycle.