Manage machine learning model versioning and compliance.
Implement and monitor ML infrastructure, optimizing resource allocation.
Support validation of AI/ML pipelines, ensuring model accuracy.
We are currently looking for a Machine Learning Administrator. The position allows for a blend of remote work and potential travel, making it versatile and accommodating.
Design, build, and maintain machine learning model productionization infrastructure.
Streamline model training, validation, and deployment in collaboration with the data science team.
Implement robust monitoring and alerting for model performance, drift, and data quality.
The Athletic delivers in-depth coverage of sports, teams, and athletes. Their newsroom of 500+ full-time staff covers hundreds of professional and college teams across North American markets and football clubs.
Design and implement MLOps pipelines to automate model training, deployment, monitoring, and management
Lead/mentor a team of MLOps Engineers, fostering an inclusive and collaborative environment that encourages innovation and continuous learning
Collaborate with Data Scientists and ML Engineers to ensure models are production-ready, scalable, and maintainable
Egen is a fast-growing and entrepreneurial company with a data-first mindset. They bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights.
Designing, deploying, and optimizing data-driven machine learning solutions on AWS.
Creating secure and scalable ML systems, enabling effective data management and model deployment.
Leading the enhancement of best practices within the data and ML lifecycle, making a substantial impact across projects and teams.
Jobgether uses an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company.
Support the full operational lifecycle of both traditional machine learning systems and emerging generative AI driven applications.
Enable scalable training, evaluation, deployment, and monitoring for a wide range of ML and GenAI workloads.
Manage model upgrades, framework versions, regression testing, maintenance tasks and maintaining performance across systems and solutions.
Achievers' employee recognition and rewards platform empowers organizations to build cultures where people feel seen and valued, everyday. They're a team of passionate, thoughtful builders with more than 4.3 million users across 190 countries, who care deeply about their product, their customers, and each other.
Engaging directly with current and prospective clients to understand business needs, translate them into technical requirements, and communicate findings in a clear, actionable way
Partnering with internal and client stakeholders to shape solutions, develop proposals, and contribute to go-to-market initiatives
Design, develop and deploy efficient data pipeline for both structured and unstructured data
Resultant consists of a team of engineers, mathematicians, data analysts, project managers, and business consultants. They partner with clients in the public and private sectors to help them overcome complex challenges, empowering clients to drive meaningful change.
Design, implement, and maintain robust, containerized, and reproducible pipelines for model training, evaluation, and deployment—across both batch and real-time settings.
Build and manage ML services, APIs, and model serving infrastructure using tools like MLflow, Amazon SageMaker, and Feature Store.
Set up and maintain monitoring, observability, and alerting systems to ensure high availability and performance (including model/data drift, feature logging, and inference latency).
AUTO1 Group Technology drives innovation in the used car market across Europe. They operate at the intersection of software engineering, data science, and DevOps, helping bring state-of-the-art ML models—such as large-scale recommendation systems and transformer-based neural networks—safely into production.
Deploy and manage AI agents and multi-agent workflows
Configure and enforce access control, permissions, and knowledge boundaries
Maintain governance standards and audit trails
SPACE44 builds and operates software systems for companies that need technology to work reliably in real, day-to-day operations. They work as long-term engineering partners, embedding experienced engineers into client environments and taking responsibility for execution, stability, and ongoing improvement of production systems.