Software Engineer (DS Team)
Job Description
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.
Nice to have
- Experience integrating MLOps tooling - experiment trackers, data versioning, and practices such as CI/CD/CT.
- Ops experience with AWS and Kubernetes.
- Data science exposure, particularly deploying and maintaining models.
Location: Tel Aviv, Israel
What You'll Get
- A strong emphasis on work-life balance.
- Beautiful offices in the heart of Tel Aviv, near the train station and main bus stops.
- Passionate, smart, and fun people to work with.
- You will never lack a challenge we are a unique blend of a fast-growing tech startup, an international firm, and an insurance company.
Apply Now
Today, every company is a digital company, reliant on technology and software to thrive and grow. This has put businesses of every size in the crosshairs of cyber criminals. Even a single incident could be catastrophic, especially for small businesses. Traditional approaches to cyber risk were failing businesses and we knew there had to be a better way. One that’s good for our insureds and our bottom line.
We designed At-Bay from the ground up to help businesses meet digital risk head on. By combining world-class technology with industry-leading insurance and cyber security expertise, we’re empowering every business, big or small, to understand and manage risk, so they can prevent attacks even before they happen.
We make your digital world safer by addressing risk differently. We question, analyze, and monitor the risk of the Cyber and Tech E+O policyholders in our portfolio—not just once, but regularly—so we can educate and advise insureds and brokers throughout our partnership.
