In short
The company that works with geospatial data at scale, using machine learning and computer vision to pioneer a new form of property information, built specifically for the organizations that finance, protect, and invest in homes, properties and businesses. They solve real problems and their product is validated by the market and have more than 50 clients already. Due to the true innovation of their product, they are backed by a number of leading venture firms and insurers.

Your mission
They would need a engineer who can bring together practices from DevOps, Data Engineering, Machine Learning Engineering, and Data Science Research to build on and improve the company’s MLOps process by removing friction, increasing efficiency, and making model development and deployment easier and faster. You will develop MLOps solutions, tools, practices and culture, enabling faster and more reliable ML solutions and build components for ML pipelines for data processing, model training, as well as monitoring. You will also contribute to the improvement of their end-to-end ML development process, from ground-truth collection tools all the way to deployment to their production environment. You will work closely together with ML team, the Data Engineering team, as well as the platform team to design and build a better and better end-to-end model development process.

The company leverages all available tools and technologies to build a best-in-class tech-stack, which affords them flexibility of fast-deployments, along with the stability to support aggressive SLAs for critical-path client APIs and applications. They build their models using Pytorch and Tensorflow, and leverage Python, Spark and Postgres across their AWS-deployed cloud infrastructure.

Your profile
– Mastery of the MLOps lifecycle management (build, deploy, and production support), ML pipelines and popular MLOps tooling.
– Demonstrated expertise in MLOps elements and architectures (data-ops, feature stores, artifact and metrics tracking).
– Advanced knowledge and significant programming experience in Python programming or other scripting language 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, Git and tools for reproducibility (e.g. virtual environments, Docker).
– Experience with cloud platforms (e.g., AWS, GCP).
– Fluent English

Company’s pitch
– Salary based on experience
– Stock options package on top
– The office in Europe is in Munich and if you live in Germany, you can work 100% remote. Some office visits would be helpful, but you can decide this yourself.
– Working in openminded and intelligent environment
– Working in multidisciplinary teams consisting of data scientists, machine learning engineers, productowners and engineers.

If you would like to hear more, please let me know and we can arrange a convenient time to speak. A CV with your experience would be nice to send over to us. I’m available at Richard.straub@digitalaccelerators.eu or +4930 217 800 71. Below is also a link to my calendar where you’re welcome to book a convenient time for you directly. https://calendly.com/richard-straub

Looking forward to hearing from you!