Spekit is building a small, high-impact ML team that tackles problems with an iterative and experimental mindset, seeking a Machine Learning Engineer in an MLOps role to design and deploy solutions that leverage user context and customer-provided content to improve productivity and outcomes.

Provide the AI/ML and computational infrastructure backbone to enable a new generation of health technologies, strategically architecting data, computing, and ML infrastructure from prototype to production. Bring MLOps expertise to bear in making architectural design decisions to ensure the resultant pipelines are scalable, robust, fault-tolerant, secure, and maintainable.

Join a team of talented developers focused on distributed backend systems and machine learning. As an MLOps Engineer, you will design and develop machine learning infrastructure and tooling to help teams deliver world class experiences. You'll build scalable services to support data integration, coach colleagues, and translate product goals into actionable engineering plans.

Drive the evolution of Enable’s cloud-native platform, ensuring scalability, reliability, and efficiency across all aspects of our SaaS infrastructure. Focus on architecting and optimizing our platform for high-performance distributed systems while also enabling machine learning (MLOps) capabilities for AI-driven applications. Collaborate across teams to develop best-in-class platform solutions that support product engineering, data engineering, and AI/ML teams.

As a Data Scientist Specialist focused on MLOPs your mission will be to own the design and deployment of scalable systems to train, monitor, and expose machine learning models in production. You will own the design of system architecture to train, retrain, develop, monitor, and expose machine learning models; Design, develop, and maintain scalable infrastructure for ML pipelines and APIs.

As our first Machine Learning Engineer focusing on MLOps, you will play a pivotal role in operationalizing our ML models, ensuring they are scalable, reliable, and easy to monitor. You’ll work closely with our data scientists, server engineers, and infrastructure teams to build feature stores for model development and enhance model deployment.

We're looking for a self-motivated, highly driven Senior Software Engineer to join our Machine Learning Operations (MLOps) team to enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, inference, and deploy ML models with higher velocity and performance, while maintaining reliability.