Senior Machine Learning Engineer (AI Insights)

This is not a role focused on building a generic chatbot. You will build the ML systems, services, evaluation frameworks, and product capabilities that make AI-powered troubleshooting and optimization reliable in real-world environments

As a Senior Machine Learning Engineer, you will design, build, and operate machine learning capabilities that power observability, troubleshooting, and optimization experiences. You will work across the full lifecycle of ML development: understanding the problem, preparing data, developing models and algorithms, defining evaluation criteria, integrating with production services, and improving performance based on real-world feedback

You will partner with software engineers, product managers, researchers, and infrastructure experts to turn ambiguous problems into reliable systems. You will have meaningful ownership of production components while contributing to the team’s technical direction and engineering practices

Build and ship production machine learning systems for infrastructure observability, troubleshooting, and optimization

Develop approaches for anomaly detection, time-series analysis, event correlation, ranking, recommendation, classification, and root-cause inference across high-volume telemetry

Create datasets, experiments, and evaluation frameworks to measure model quality, robustness, usefulness, and failure modes

Build data pipelines, feature-generation workflows, inference services, and feedback loops for continuous improvement

Integrate ML capabilities with telemetry platforms and customer-facing experiences, including Mission Control, Grafana, and related observability services

Collaborate with engineers working on metrics, logs, traces, telemetry enrichment, platform APIs, and data infrastructure

Monitor and improve the quality, latency, reliability, and cost of ML-powered services in production

Investigate data and model failures, identify root causes, and implement durable fixes

Make thoughtful trade-offs across model quality, interpretability, operational complexity, latency, and cost

Contribute to technical designs, code reviews, testing standards, and operational practices for the team

Partner with Staff engineers and technical leaders to break down complex initiatives and deliver incrementally

Share knowledge through documentation, mentorship, and collaboration with engineers across CoreWeave

Work on foundational AI capabilities for an AI-native cloud company

Solve difficult ML problems using high-volume, high-value infrastructure telemetry

Help engineers and customers understand, troubleshoot, and optimize large-scale AI workloads

Own meaningful production systems at the intersection of machine learning, observability, and distributed systems

Work closely with experienced engineers, researchers, product managers, and infrastructure experts

See your work move from experimentation into products used by CoreWeave engineers and customers- Strong debugging and systems-thinking skills, including the ability to reason about data quality, distributed systems, latency, and operational failure modes

Several years of experience designing and shipping machine learning systems that operate in production

A track record of owning complex technical work and delivering results with appropriate guidance and autonomy

Experience building reliable data and inference services, not only notebooks or offline prototypes

Solid understanding of machine learning fundamentals, including model selection, feature engineering, experimentation, evaluation, and failure analysis

Experience taking an ML capability from an initial hypothesis through production launch and iteration

Excellent communication and collaboration skills, with the ability to work effectively across engineering, research, product, and infrastructure teams

Strong software engineering skills in Python; experience with Go or another systems-oriented language is a plus

Experience working with time-series, event, log, metric, trace, or other operational data

Experience defining and implementing evaluation methodology for ambiguous or domain-specific ML problems

Experience with observability platforms or technologies such as Grafana, Prometheus, VictoriaMetrics, ClickHouse, Loki, or Kafka

Experience with Kubernetes and cloud infrastructure, especially for telemetry, logging, or application observability

Experience with anomaly detection, incident intelligence, search, recommendations, or ranking systems

Experience with large language model evaluation, post-training, retrieval, tool use, or grounded generation—particularly when combined with structured telemetry and deterministic systems

Experience building ML products for infrastructure, developer tools, reliability engineering, or other technical users

Familiarity with human-in-the-loop workflows, access control, auditability, and safety requirements for operational systems