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At-Bay is a venture-backed insurance company (raised ~$300M from Icon Ventures, Lightspeed, Khosla, Microsoft's M-12, and others) built by cybersecurity experts. The company combines insurance expertise with deep technical knowledge to help businesses understand and manage digital risk. With 100+ employees across San Francisco, New York, Atlanta, and Tel Aviv, At-Bay is scaling aggressively.
You will join the Data Science team as a Software Engineer specializing in machine learning infrastructure. Your core responsibility is owning the engineering quality of ML systems—bridging the gap between research and production. You'll write production-grade code, build and maintain ML services, and ensure our models run reliably at scale.
Key responsibilities include: transitioning ML workloads from research to production with a focus on scalability and reliability; designing and maintaining ML services, infrastructure, and monitoring; integrating ML into production systems alongside engineering and DevOps teams; owning data infrastructure and tooling that ML systems depend on; and mentoring data scientists to write cleaner, more maintainable code while raising engineering standards across the team.
You bring 4+ years of software engineering experience with hands-on ML domain work. You have strong Python fundamentals and deep knowledge of software engineering principles (OOP, design patterns, SOLID, clean code, architecture). You hold a Bachelor's degree or higher in Computer Science or a STEM field. You understand core ML concepts well enough to productionize models, have experience building services that track the ML lifecycle, and know data cleaning and wrangling. AWS (particularly EKS) and Kubernetes experience is required. Nice-to-haves include MLOps tooling integration, broader AWS/Kubernetes ops experience, and data science exposure.
The role is based in Tel Aviv, in a modern office near the train station and main bus stops. At-Bay emphasizes work-life balance and offers a collaborative environment blending startup speed, international scale, and insurance domain complexity.
