5-7 years of experience in software engineering with a focus on cloud infrastructure and machine learning.
Proven track record of building SaaS products that handle large volumes of data.
Experience working directly with enterprise customers to address complex software needs.
Strong background in designing and deploying large-scale machine learning systems.
Ability to manage the intricacies of ML deployment, including evaluation and monitoring.
Experience with LLM-powered data systems, particularly in research workflows.
Responsibilities
Lead the transformation of Axiom into a premier engineering company specializing in enterprise ML software.
Design and construct the foundational infrastructure for enterprise ML systems, focusing on model evaluation and customer data management.
Architect scalable systems for the storage and retrieval of diverse data types, including chemical and clinical data.
Deploy advanced reasoning agents from research to production, integrating them into customer-facing products.
Mentor and empower scientists in ML, chemistry, and biology to foster a strong engineering culture.
Benefits
Opportunity to work at the forefront of enterprise machine learning technology.
Collaborative environment focused on team growth and engineering excellence.
Engagement with large enterprise customers, enhancing problem-solving skills.
Chance to influence the engineering culture across the organization.
Access to cutting-edge tools and technologies in ML and data systems.
Full Job Description
What you will be doing:
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
Key criteria:
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
