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 build the 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 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. What you’ll do 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. What we’re looking for 5+ years of experience designing and shipping machine learning systems that operate in production. Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a related technical field Strong software engineering skills in Python; experience with Go or another systems-oriented language is a plus. Solid 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. Experience building reliable data and inference services, not only notebooks or offline prototypes. Experience defining and implementing evaluation methodology for ambiguous or domain-specific ML problems. Strong debugging and systems-thinking skills, including the ability to reason about data quality, distributed systems, latency, and operational failure modes. 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. A track record of owning complex technical work and delivering results with appropriate guidance and autonomy. 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 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. 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 $165,000 to $242,000. 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
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.

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 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.

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