About the role
About At-Bay We are an insurance company built by cybersecurity experts, leveraging our technology and security expertise to better understand digital risks and, thus, build better insurance products and services. We help clients by providing insurance products, guidance on their security issues, and active risk management. Like software, digital risk is eating the world and we aim to empower businesses to thrive in this ever-changing landscape. At-Bay has raised nearly $300MM in funding from Icon Ventures, LightSpeed Venture Partners, Qumra Capital, M-12 (Microsoft), Acrew Capital, Khosla Ventures, Shlomo Kramer and MunichRe. We are 100+ people distributed globally with hubs in San Francisco, New York City, Atlanta, and Tel Aviv. We have aggressive plans to scale our business and believe the best way to make that happen is to immediately invest in further developing our teams.
About
the role We're looking for a software engineer who specializes in machine learning - someone whose core strength is writing excellent, production-grade code. You'll own the engineering quality of our ML systems: building, maintaining, and monitoring the services that run our models in production, and helping our data scientists ship code that's readable, maintainable, and built to last.
Responsibilities Transition ML workloads from research to production - ensuring scalability, efficiency, and reliability. Design, build, and maintain ML services, their infrastructure, and monitoring across their lifecycle. Integrate ML into our production systems, working closely with the engineering and devops teams. Own the data infrastructure and tooling our ML systems rely on, and shape unstructured data into a form ready for analysis. Help data scientists write readable, maintainable code, and raise software engineering standards across
the team.
Qualifications 4+ years as a software engineer, with some hands-on experience in the ML domain. Strong Python and software-engineering fundamentals: OOP, design patterns, SOLID, clean code, and architecture. Bachelor's degree or higher in Computer Science or another STEM field. Solid grasp of core ML concepts (e.g., linear regression) - you understand the models you put into production. Experience building services and tools that track the ML lifecycle and optimize ML workloads. Working knowledge of data cleaning and wrangling, and the right tools for the job. Experience with AWS (concretely EKS). Driven and result-oriented. A solid track record of execution, with strong attention to detail.
