Jarmin
AI-powered ML engineering service that provides 24/7 machine learning engineering capabilities to build production ML systems.
At a Glance
Pricing
Paid
Engagement
Available On
About Jarmin
Jarmin provides AI-powered machine learning engineering services that function as 24/7 ML engineer employees for companies. Built by a team with experience from Meta, Apple, AWS, Lockheed Martin, and JPMorganChase, Jarmin aims to democratize access to sophisticated ML engineering capabilities that were previously only available to companies with large budgets and access to rare talent.
The platform addresses the critical bottleneck in AI development: the scarcity of ML engineering talent. Rather than competing for the limited pool of ML engineers, companies can leverage Jarmin's system to decompose requirements, architect robust systems, make informed tradeoffs, and ship production pipelines.
- AI-Powered ML Engineering - Provides machine learning engineering capabilities that think like exceptional ML engineers, handling complex system design and implementation
- Production Pipeline Development - Ships production-ready ML pipelines, moving beyond prototypes to deployable systems
- Ambiguous Requirement Handling - Decomposes unclear or complex requirements into actionable engineering tasks
- Robust System Architecture - Designs and implements ML systems with proper architecture and informed tradeoffs
- Y Combinator Backed - Supported by Y Combinator, providing credibility and resources for growth
To get started with Jarmin, teams can scope their first project directly with the founders through their website. The service is designed for ambitious teams with meaningful problems who want to build exceptional AI capabilities without the traditional barriers of hiring specialized ML talent.

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Pricing
Custom Project
Scope your first project with the Founders
- 24/7 ML engineering capabilities
- Production pipeline development
- System architecture
- Requirement decomposition
Capabilities
Key Features
- 24/7 ML engineering capabilities
- Production pipeline development
- Ambiguous requirement decomposition
- Robust system architecture
- Informed tradeoff decisions
- ML system implementation