Platform Engineer

About the role

Axiom is transforming drug discovery and development by replacing animal testing with advanced technology. We build an ecosystem that uses proprietary data and machine learning models to improve how medicines are discovered. This role involves developing the core infrastructure for our enterprise ML software, which is already used by major pharmaceutical companies.

Key facts

What you'll do

• Guide Axiom's growth into a leading engineering company specializing in enterprise ML software.
• Create the essential infrastructure for Axiom's enterprise ML systems, including model evaluation, deployment, inference, serving, and customer data handling.
• Design scalable systems for storing, retrieving, and inferring chemical, biological, and clinical data.
• Transition large-scale reasoning agents from research to production, integrating them into customer-facing products and on-premise infrastructure.
• Foster a strong engineering culture and mentor scientists in machine learning, chemistry, and biology to enhance their engineering skills.

Requirements

• Proven experience as a generalist software engineer, covering cloud infrastructure, machine learning, backend systems, and distributed systems.
• Enjoy working with enterprise clients and simplifying intricate technical solutions for them.
• Track record of building and deploying production systems for large enterprise businesses.
• Committed to team development, especially in cultivating a robust engineering culture throughout the company.
• Eager to collaborate with researchers and scientists, helping them develop strong engineering capabilities.
• Takes complete responsibility for the customer experience, with a strong focus on reliability and anticipating potential issues.

Nice to have

• Experience creating SaaS products that manage and process substantial volumes of customer data.
• Direct experience supporting the complex software requirements of large enterprise customers.
• Background in designing and developing large-scale machine learning systems, from data access to training, evaluation, and deployment.
• Familiarity with the practical aspects of ML deployment, such as evaluation pipelines, version control, and monitoring.
• Experience building LLM-powered data systems, particularly for research workflows and information retrieval.

Skills & tools

• Cloud infrastructure
• Machine learning
• Backend systems
• Distributed systems
• Enterprise ML software
• Model deployment
• Model inference
• Data management
• Large-scale reasoning agents
• LLMs

Practical notes

• Not specified