MOUNTAIN VIEW, CA/REMOTE /ENGINEERING /FULL-TIME

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CAPE Analytics provides instant property intelligence for buildings across the United States. CAPE Analytics enables insurers and other property stakeholders to access valuable property attributes at time of underwriting, with the accuracy and detail that traditionally required an on-site inspection, but with the speed and coverage of property record pre-fill. Founded in 2014, CAPE Analytics is backed by leading venture firms and innovative insurers and is comprised of computer vision, data science, and risk analysis experts.

A BIT ABOUT US

Since our founding in 2014, CAPE Analytics has used machine learning and computer vision to pioneer a new form of property information, built specifically for the organizations that finance, protect, and invest in our homes and businesses. Our 100+ (and rapidly growing!) clients across insurance and real estate are leading a digital transformation to secure properties and livelihoods in the face of complex trends in housing and climate.

THE OPPORTUNITY

As an MLOps Engineer on CAPE’s Machine Learning team, you’ll collaborate with Data Scientists, Computer Vision/Machine Learning Engineers, Data Engineers, and members across Software Engineering, Product, and Sales teams to enable the development of robust, scalable machine learning models for identification and annotation of the built world.

We need an engineer that can bring together practices from DevOps, Data Engineering, Machine Learning Engineering, and Data Science Research to build on and improve our MLOps process by removing friction, increasing efficiency and making model development and deployment easier and faster.

THE TECH STACK

CAPE leverages all available tools and technologies to build our best-in-class tech-stack, which affords us flexibility of fast-deployments, along with the stability to support aggressive SLAs for critical-path client APIs and applications. We use a commercial MLOps platform deployed on Kubernetes and build all the infrastructure that goes around it. Our models are both deep learning and tabular machine learning models (and sometimes both at the same time).

YOUR PROFILE

A professional MLops Engineer with solid technical background, excelling in understanding complex problems and processes and proactively proposing efficient solutions. You thrive in a collaborative environment and take the initiative to solve outstanding issues and to improve existing processes. You excel at communicating with different stakeholders and collaborators, technical and non-technical, and you love building practical and state-of-the-art solutions centered around Machine Learning. You strive to stay up to date with the latest best practices and tools by researching and participating in communities and technical conferences.

WITHIN 1 MONTH, YOU’LL

Develop MLOps solutions using the most appropriate tools enabling faster and more reliable ML products.
Build components for ML pipelines for data processing, model training, as well as monitoring.
Build and administer CI/CD processes for ML components and pipelines.
Contribute to developing best practices around building and deploying ML models.

WITHIN 3 MONTHS, YOU’LL

Contribute to the improvement of our end-to-end ML development process, from ground-truth collection tools all the way to deployment to our production environment.

WITHIN 6 MONTHS, YOU’LL

Work with our ML team, our Data Engineering team, as well as our Platform team to design and build a better and better end-to-end model development process.
Additional responsibilities to be assigned based on performance.

THE SKILL SET

BS in Computer Science or other quantitative discipline.
5-8 years of total experience with 3+ years of that experience working with tools and solutions for the Machine Learning lifecycle (build, deploy, and production support), ML pipelines and popular MLOps tooling.
Some experience with Kubernetes clusters, preferably on Amazon EKS. Familiarity with DevOps and CI/CD (Github Actions, Jenkins, other equivalent tools).
Knowledge of MLOps elements and architectures (data-ops, feature stores, artifacts and metrics tracking).
Significant programming experience in Python programming and other scripting languages including relevant libraries and best practices (e.g., unit tests, CI/CD).
Solid knowledge of containers and orchestration tools (e.g., Docker, K8s).
Broad understanding of the process of development of an ML solution.
Familiarity with the Linux environment including shell scripting and with Git.
Experience with cloud platforms (e.g., AWS, GCP).

Cape Analytics GmbH

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