Platform Engineer

Axiom is building a compounding ecosystem to replace animal testing and, over time, reshape how clinical trials are run. It starts with deeply understanding the needs of drug hunters inside large pharma. Those needs shape the world-class datasets we build from scratch. We then use that data to advance our own ML research, while also collaborating with leading AI labs to improve frontier models’ ability to reason over Axiom’s data inside Axiom’s agent harness. This creates a compounding loop: deeper customer understanding shapes the data we generate; better data improves frontier models, Axiom’s fine-tuned models, and our agentic infrastructure; stronger models and tooling expand the capabilities we can offer; and those capabilities are forward deployed into pharma's drug discovery workflows, where scientists use them to solve the highest value drug discovery problems. In turn, this helps us identify the next problems to tackle. Today, we are focused on solving drug-induced liver injury through an integrated data and agentic system already being used by 7 of the top 20 pharma companies and several of the world’s most innovative biotechs. Over time, Axiom will build the world’s largest human datasets across all the major organ systems, paired with an agentic harness that uses this data to predict human drug outcomes dramatically better than animals.

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  Lead Axiom’s evolution into a world-class engineering company focused on enterprise ML software

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  Design and build the core infrastructure that powers Axiom’s enterprise ML systems, including model evaluation/deployment, model inference/serving, and customer data management

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  Architect scalable systems for inference, storage, and retrieval of chemical, biological, and clinical data

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  Deploy large-scale reasoning agents from research environments into production, integrating them into on-prem customer-facing products and infrastructure

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  Teach and empower scientists across ML, chemistry, and biology to become great engineers by instilling a great engineering culture

• 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

• 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