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# Platform Engineer

- **Company:** Axiom
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Clinical Data Repository, Software as a Service, Cloud Computing, Customer Data Management, Data Security, Data Systems, Distributed Systems, Information Retrieval, Machine Learning, Management of Software Versions, Large Language Models, Model Validation, Backend, Build Management, Machine Learning Operations
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/platform-engineer-axi-om-9017893

## About the Role

* Strong generalist software engineer with experience across cloud infrastructure,machine learning, backend systems, distributed systems
 * Enjoys working with enterprise customers and simplifying complex technical solutions to meet their needs
 * Built and deployed production systems used by large enterprise businesses
 * Invested in team growth particularly when it comes to building strong engineering culture across the company
 * Passionate about collaborating with researchers and scientists, helping them become strong engineers
 * Takes full ownership of the customer experience-deeply focused on reliability and all the ways things can go wrong
 * Demonstrates relentless

## Description

* Lead Axiom's evolution into a world-class engineering company focused on enterprise ML software
 * Design and build the core infrastructure that powers Axiom's enterprise ML systems, including model evaluation/deployment, model inference/serving, and customer data management
 * Architect scalable systems for inference, storage, and retrieval of chemical, biological, and clinical data
 * Deploy large-scale reasoning agents from research environments into production, integrating them into on-prem customer-facing products and infrastructure
 * Teach and empower scientists across ML, chemistry, and biology to become great engineers by instilling a great engineering culture

Various expertise which gets us interested:

 * Built SaaS products that store and process large volumes of customer data.
 * Worked directly with large enterprise customers and supported their complex software needs
 * Designed and developed large-scale machine learning systems covering data access, training, evaluation, and deployment
 * Handled the "messy" parts of ML deployment, such as evaluation pipelines, versioning, and monitoring
 * Built LLM-powered data systems, with a focus on research workflows and information retrieval


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