Machine Learning Engineer
4 dni temu
About Kalepa
Kalepa is revolutionizing the commercial insurance industry by leveraging cutting-edge technology to empower underwriters with data-driven insights. Our state-of-the-art AI Copilot platform enables underwriters to make informed, timely, and accurate decisions. By harnessing the power of artificial intelligence, Kalepa's clients have seen significant improvements in their top and bottom lines.
Our team members bring expertise from top tech companies, including Facebook, Google, Amazon, Mastercard, and Uber. Backed by leading venture capitalists like IA Ventures and Inspired Capital, Kalepa is poised for rapid growth and innovation.
Our Values
- Hustle & Grit: We expect excellence and relentless pursuit of craft from every member of our team.
- Customer Focus: We prioritize work that drives client growth, profitability, and efficiency.
- Meredithocracy: Exceptional talent excels here, with rapid promotions and compensation growth.
- Transparency: We are honest with each other, eliminating back-office politics and secrecy.
- Experimentation: We tackle novel problems through rapid testing, measurement, and iteration.
About the Role
Kalepa seeks a seasoned Machine Learning Engineer to lead the development, deployment, and scaling of machine learning models. This role requires ownership of projects from inception to delivery, collaborating closely with Product Management and Software Engineers. The ideal candidate will drive project direction, maintain focus, and deliver results within two-week sprints.
Key Responsibilities
- Tackle vast amounts of structured and unstructured data from multiple sources, transforming it into valuable insights about behavior and risk.
- Design, build, and deploy machine learning models in collaboration with a small team, tackling clients' questions and driving business outcomes.
- Work closely with full-stack, data, and DevOps engineers to bring machine learning models to production.
Requirements
- 2+ years of experience in applied machine learning algorithms, NLP, statistics, and Python.
- Experience deploying models and algorithms in production.
- Comfort with data science and engineering required to bring models to production.
- AWS experience (nice-to-have).
- Data analytics and data engineering experience (nice to have).
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