classification Jobs

CoreWeave is seeking a Senior Machine Learning Engineer to join the AI Insights team. This role focuses on building customer-facing AI capabilities that leverage machine learning, observability, and production software engineering. You will develop ML systems, services, evaluation frameworks, and product capabilities to enable reliable AI-powered troubleshooting and optimization in real-world environments. The position involves working across the full ML development lifecycle, from problem understanding and data preparation to model development, integration, and performance improvement based on feedback. You will collaborate with cross-functional teams to transform complex problems into robust systems and will have significant ownership of production components while influencing the team's technical direction.

$165k - $242k

CoreWeave is seeking a Staff Machine Learning Engineer to join the AI Insights team. This role will be a technical leader responsible for defining and implementing machine learning systems that power anomaly detection, signal correlation, incident understanding, recommendations, and other intelligence capabilities across CoreWeave’s observability and cloud platforms. You will work across the full machine learning lifecycle, from problem framing and model development to evaluation, service building, and scaled operation. The position involves close collaboration with software engineers, product managers, researchers, and domain experts to transform innovative ideas into reliable customer experiences, focusing on building underlying ML systems for AI-powered troubleshooting and optimization in real-world environments.

$180k - $250k

Parallel Wireless is seeking a Senior Applied AI Evaluation Engineer to enhance how their RAN R&D organization analyzes engineering tickets. This role will focus on improving Root-Cause Analysis, recommending ownership, and learning from resolved cases by building rigorous evaluation datasets, establishing baselines, and comparing internal and external tools. The primary goal is measurable, evidence-based improvement, assessing the accuracy, grounding, calibration, and usefulness of AI-generated conclusions for engineering teams. A secondary focus involves analyzing engineering workflows to identify bottlenecks and opportunities for process improvement.