Job description

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com . About CoreWeave CoreWeave is an AI hyperscaler building the cloud infrastructure and services that power the next generation of artificial intelligence. Our customers run demanding training, inference, and high-performance workloads, and our teams build the systems needed to operate that infrastructure reliably at scale. About the team The AI Insights team builds customer-facing AI capabilities across CoreWeave’s Mission Control portfolio. We combine machine learning, observability, and production software engineering to help engineers and customers understand workload health, diagnose infrastructure issues, and identify opportunities to improve efficiency, capacity, and performance. Our work spans telemetry from metrics, logs, traces, alerts, and operational events. We are building the intelligence layer that turns this data into trustworthy, actionable insights—grounded in evidence and integrated into the tools where people operate CoreWeave infrastructure. This is not a role focused on building a generic chatbot. You will help build the underlying ML systems, services, evaluation frameworks, and product capabilities that make AI-powered troubleshooting and optimization reliable in real-world environments. About the role As a Staff Machine Learning Engineer, you will be a technical leader on the AI Insights team. You will define and implement the 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 lifecycle: framing problems, developing models and algorithms, designing evaluation methodology, building data and inference services, integrating with production systems, and operating the result at scale. You will partner closely with software engineers, product managers, researchers, and domain experts to turn promising ideas into reliable customer experiences. What you’ll do Lead the technical design and delivery of 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. Build evaluation frameworks and datasets that measure accuracy, usefulness, robustness, and safety in real operational scenarios. Translate research and prototypes into maintainable, scalable services with clear operational ownership. Design data pipelines, feature-generation workflows, model-serving paths, and feedback loops for continuous improvement. Integrate ML capabilities with telemetry platforms and customer-facing experiences, including Mission Control, Grafana, and related observability services. Establish practical standards for experimentation, offline and online evaluation, monitoring, reproducibility, and model lifecycle management. Make thoughtful trade-offs across model quality, latency, cost, interpretability, reliability, and ease of operation. Work with teams responsible for metrics, logs, traces, telemetry enrichment, and platform APIs to create coherent cross-system intelligence. Mentor engineers and raise the technical bar for machine learning and production engineering across the organization. Communicate technical decisions clearly and influence roadmaps across teams without relying on formal authority. What we’re looking for Significant experience designing and shipping machine learning systems that operate in production. Strong software engineering skills in Python; experience with Go or another systems-oriented language is a plus. Deep understanding of machine learning fundamentals, including model selection, feature engineering, experimentation, evaluation, and failure analysis. Experience working with time-series, event, log, metric, trace, or other operational data at meaningful scale. Demonstrated ability to define evaluation methodology for ambiguous or domain-specific ML problems. Strong systems thinking and the ability to reason about distributed systems, data quality, latency, observability, and operational failure modes. Excellent communication and collaboration skills, including the ability to work effectively with research, product, infrastructure, and customer-facing teams. A track record of technical leadership, mentorship, and influence across organizational boundaries. Nice to have 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. Why this role Work on foundational AI capabilities for an AI-native cloud company. Solve difficult ML problems using high-volume, high-value infrastructure telemetry. Help define how engineers and customers understand, troubleshoot, and optimize large-scale AI workloads. Influence the technical direction of a growing team at the intersection of machine learning, observability, and distributed systems. Turn applied research and engineering ideas into production products used by CoreWeave engineers and customers. Lead by example while helping establish the team’s engineering and ML practices. Why CoreWeave? At CoreWeave, we work hard, have fun, and move fast! We’re in an exciting stage of hyper-growth that you will not want to miss out on. We’re not afraid of a little chaos, and we’re constantly learning. Our team cares deeply about how we build our product and how we work together, which is represented through our core values: Be Curious at Your Core Act Like an Owner Empower Employees Deliver Best-in-Class Client Experiences Achieve More Together We support and encourage an entrepreneurial outlook and independent thinking. We foster an environment that encourages collaboration and enables the development of innovative solutions to complex problems. As we get set for takeoff, the organization's growth opportunities are constantly expanding. You will be surrounded by some of the best talent in the industry, who will want to learn from you, too. Come join us! The base salary range for this role is $180,000 to $250,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility). What We Offer The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location. In addition to a competitive salary, we offer a variety of benefits to support your needs. The benefits below reflect our US-based offerings for full-time employees; for roles in other locations, benefits vary and are shared during the hiring process. These include: Medical, dental, and vision insurance - 100% paid for by CoreWeave Company-paid Life Insurance Voluntary supplemental life insurance Short and long-term disability insurance Flexible Spending Account Health Savings Account Tuition Reimbursement Ability to Participate in Employee Stock Purchase Program (ESPP) Mental Wellness Benefits through Spring Health Family-Forming support provided by Carrot Paid Parental Leave Flexible, full-service childcare support with Kinside 401(k) with a generous employer match Flexible PTO Catered lunch each day in our office and data center locations A casual work environment A work culture focused on innovative disruption California Applicants California Consumer Privacy Act Equal Opportunity & Accommodations CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants and candidates will receive consideration for employment without regard to race, color, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information. As part of this commitment and consistent with the Americans with Disabilities Act (ADA) , CoreWeave will ensure that qualified applicants and candidates with disabilities are provided reasonable accommodations for the hiring process, unless such accommodation would cause an undue hardship. If reasonable accommodation is needed, please contact: careers@coreweave.com . Export Control Compliance This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency. CoreWeave may, for legitimate business reasons, decline to pursue any export licensing process.

Requirements

Must-haves

What you’ll do
Lead the technical design and delivery of 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.
Build evaluation frameworks and datasets that measure accuracy, usefulness, robustness, and safety in real operational scenarios.
Translate research and prototypes into maintainable, scalable services with clear operational ownership.
Design data pipelines, feature-generation workflows, model-serving paths, and feedback loops for continuous improvement.
Integrate ML capabilities with telemetry platforms and customer-facing experiences, including Mission Control, Grafana, and related observability services.
Establish practical standards for experimentation, offline and online evaluation, monitoring, reproducibility, and model lifecycle management.
Make thoughtful trade-offs across model quality, latency, cost, interpretability, reliability, and ease of operation.
Work with teams responsible for metrics, logs, traces, telemetry enrichment, and platform APIs to create coherent cross-system intelligence.
Mentor engineers and raise the technical bar for machine learning and production engineering across the organization.
Communicate technical decisions clearly and influence roadmaps across teams without relying on formal authority.

Nice-to-haves

Strong software engineering skills in Python; experience with Go or another systems-oriented language is a plus.
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.
Why this role
Work on foundational AI capabilities for an AI-native cloud company.
Solve difficult ML problems using high-volume, high-value infrastructure telemetry.
Help define how engineers and customers understand, troubleshoot, and optimize large-scale AI workloads.
Influence the technical direction of a growing team at the intersection of machine learning, observability, and distributed systems.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythongokubernetes

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Parental Leave

Wellness