Shield is a global startup, with offices in TLV, NYC, LDN, and LIS.
We’re rapidly growing and looking for another important piece of the puzzle.
Is it you?
We are looking for a highly skilled and motivated Machine Learning Engineer to join our Data Science Engineering team. The ideal candidate will have a deep understanding of machine learning infrastructure and solutions, enabling data scientists and engineers to efficiently develop, deploy, and maintain models in production by delivering a scalable and high-quality solution. This role requires a combination of technical expertise, strong problem-solving skills, and the ability to collaborate effectively with cross-functional teams.
About the Data Science Group: The Data Science group at Shield is at the forefront of developing and implementing advanced machine learning models and solutions. Our team is composed of talented data scientists, engineers, and subject matter experts who work together to create a high-quality, production-ready surveillance product and services. We are committed to pushing the boundaries of technology and delivering impactful results for our clients.
Let’s get down to business:
What You’ll Do:
- Develop and maintain machine learning infrastructure and solutions to support the efficient development, deployment, and maintenance of models in production.
- Collaborate with data scientists, engineers, and subject matter experts to deliver high-quality, production-ready machine learning models.
- Design and implement software architecture for machine learning solutions.
- Optimize model deployment and serving using industry best practices with CPU, GPU, and external APIs.
- Work with containerized environments such as Kubernetes and Docker.
- Utilize AWS for cloud-based machine learning solutions.
- Conduct unit testing and ensure continuous integration and continuous deployment (CI/CD) practices are followed.
- Collaborate with the DevOps team to establish best practices for MLOps.
- Work in a production environment and closely with data science teams to ensure seamless model deployment and maintenance.
- Stay updated with the latest advancements in machine learning and related technologies.
Experience and Skills:
- A deep understanding of machine learning infrastructure and solutions.
- 4+ years of Python programming experience – MUST.
- 3+ years of experience as a Machine Learning Engineer, including experience in deploying machine learning models into production at scale – MUST.
- Experience with software architecture design
- Deep familiarity with industry best practices for optimized model deployment and serving with CPU, GPU, and external APIs.
- Background in Linux and containerized environments such as Kubernetes and Docker – MUST.
- Experience with Cloud
- Experience with tools such as ElasticSearch, RabbitMQ, Kafka, and Redis.
- Experience with unit testing and CI/CD.
- Experience in MLOps – Advantage.
- Experience working in a production environment and with data science teams – Advantage.
- Ability to collaborate with the DevOps team around best practices of MLOps – Advantage.
- Experience with NLP and Speech models – Advantage.
- Self-motivated, team player, action and results-oriented.
- Well organized, with excellent communication and reporting skills.
Oh hey, you made it all the way here!
So, in case you were wondering, Shield is how compliance teams in financial services can finally read between the lines to see what their employee communications are really saying.
Our platform analyzes digital interactions to fight financial crimes and mitigate a toxic workplace environment.
Shield is a post Series B startup ($35M) with some of the largest financial organizations in the world as investors and customers.
Shielders listen more intently. Pay closer attention to the details. Make the extra effort. Care. It’s what we do at Shield every day. And not just for our customers, but for everyone we work with. It’s all about creating a world where people understand and trust each other.
Shield is set to do good in the world, we help protect market integrity and people’s financial assets.