Machine Learning Engineer

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.

Responsibilities

• Design and develop ML applications, infra and manage their lifecycle
• Work closely with the engineering team to integrate your work into our production systems.
• Transition machine learning models from research to production, ensuring scalability, efficiency, and reliability.
• Create, manage and monitor data infrastructures and tools, including collecting, storing, processing and analyzing our data and data systems.
• Gather and process raw data at scale. Process unstructured data into a form suitable for analysis.
  
  

• 3+ years of experience in SWE, of them 2+ years of experience as MLOps or MLE.
• Rich programming experience in Python, and relevant frameworks (PyTorch, Huggingface, AWS etc.)
• Bachelor’s Degree or higher in Computer Science or other STEM fields.
• Experience with LLM integration best practices (LLMOps) and how to productize them.
• Experience in integrating MLOps tools into existing solutions (experiment trackers, data versioning and devops concepts such as CI/CD/CT)
• Knowledge in Data Science - specifically - deploying and maintaining models.
• Knowledge of data cleaning, wrangling, visualization and reporting, with an understanding of the best, most efficient use of associated tools and applications to complete these tasks.
• Experience in data mining and machine learning.
• Experience with processing large amounts of structured and unstructured data, including integrating data from multiple sources.
• Experience with implementing services and tools that track the ML lifecycle, and optimize ML workloads.
  
  

• Driven and result oriented
• A solid track record of data management showing execution capability and attention to detail.