{"id":1229717,"url":"https://alion.io/job/coreweave-senior-machine-learning-engineer-ai-insights","title":"Senior Machine Learning Engineer, AI Insights","company":{"id":7348,"name":"CoreWeave","domain":"coreweave.com","url":"https://alion.io/company/coreweave","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":77,"open_postings":71,"ghost_share":0,"stale_share":0.732,"repost_share":0,"time_to_fill_p50_days":61,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":165000,"max":242000,"currency":"USD","period":"year","gross":null,"usd_annual":242000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"CoreWeave","optional":false},{"name":"Go","optional":false},{"name":"Grafana","optional":false},{"name":"Interpretability","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Apache Kafka","optional":true},{"name":"ClickHouse","optional":true},{"name":"Function Calling","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Kubernetes","optional":true},{"name":"Loki","optional":true},{"name":"Outlook","optional":true},{"name":"Post-training","optional":true},{"name":"Prometheus","optional":true},{"name":"Tool Use","optional":true},{"name":"VictoriaMetrics","optional":true}],"status":"live","first_seen_at":"2026-02-02T14:56:40Z","employer_posted_date":"2026-09-17","last_verified_at":"2026-10-12T14:23:21Z","board_verified":true,"closed_at":null,"days_open":251,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":251},"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.\nAbout CoreWeave\nCoreWeave 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.\nAbout the team\nThe 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.\nOur 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.\nThis 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.\nAbout the role\nAs 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.\nYou 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.\nWhat you’ll do\nBuild and ship production machine learning systems for infrastructure observability, troubleshooting, and optimization.\nDevelop approaches for anomaly detection, time-series analysis, event correlation, ranking, recommendation, classification, and root-cause inference across high-volume telemetry.\nCreate datasets, experiments, and evaluation frameworks to measure model quality, robustness, usefulness, and failure modes.\nBuild data pipelines, feature-generation workflows, inference services, and feedback loops for continuous improvement.\nIntegrate ML capabilities with telemetry platforms and customer-facing experiences, including Mission Control, Grafana, and related observability services.\nCollaborate with engineers working on metrics, logs, traces, telemetry enrichment, platform APIs, and data infrastructure.\nMonitor and improve the quality, latency, reliability, and cost of ML-powered services in production.\nInvestigate data and model failures, identify root causes, and implement durable fixes.\nMake thoughtful trade-offs across model quality, interpretability, operational complexity, latency, and cost.\nContribute to technical designs, code reviews, testing standards, and operational practices for the team.\nPartner with Staff engineers and technical leaders to break down complex initiatives and deliver incrementally.\nShare knowledge through documentation, mentorship, and collaboration with engineers across CoreWeave.\nWhat we’re looking for\nSeveral years of experience designing and shipping machine learning systems that operate in production.\nStrong software engineering skills in Python; experience with Go or another systems-oriented language is a plus.\nSolid understanding of machine learning fundamentals, including model selection, feature engineering, experimentation, evaluation, and failure analysis.\nExperience working with time-series, event, log, metric, trace, or other operational data.\nExperience building reliable data and inference services, not only notebooks or offline prototypes.\nExperience defining and implementing evaluation methodology for ambiguous or domain-specific ML problems.\nStrong debugging and systems-thinking skills, including the ability to reason about data quality, distributed systems, latency, and operational failure modes.\nExperience taking an ML capability from an initial hypothesis through production launch and iteration.\nExcellent communication and collaboration skills, with the ability to work effectively across engineering, research, product, and infrastructure teams.\nA track record of owning complex technical work and delivering results with appropriate guidance and autonomy.\nNice to have\nExperience with observability platforms or technologies such as Grafana, Prometheus, VictoriaMetrics, ClickHouse, Loki, or Kafka.\nExperience with Kubernetes and cloud infrastructure, especially for telemetry, logging, or application observability.\nExperience with anomaly detection, incident intelligence, search, recommendations, or ranking systems.\nExperience with large language model evaluation, post-training, retrieval, tool use, or grounded generation-particularly when combined with structured telemetry and deterministic systems.\nExperience building ML products for infrastructure, developer tools, reliability engineering, or other technical users.\nFamiliarity with human-in-the-loop workflows, access control, auditability, and safety requirements for operational systems.\nWhy this role\nWork on foundational AI capabilities for an AI-native cloud company.\nSolve difficult ML problems using high-volume, high-value infrastructure telemetry.\nHelp engineers and customers understand, troubleshoot, and optimize large-scale AI workloads.\nOwn meaningful production systems at the intersection of machine learning, observability, and distributed systems.\nWork closely with experienced engineers, researchers, product managers, and infrastructure experts.\nSee your work move from experimentation into products used by CoreWeave engineers and customers.\nWhy CoreWeave?\nAt 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: \nBe Curious at Your Core\nAct Like an Owner\nEmpower Employees\nDeliver Best-in-Class Client Experiences\nAchieve More Together\nWe 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! \nThe 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). \nWhat We Offer\nThe 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.\nIn 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:\nMedical, dental, and vision insurance - 100% paid for by CoreWeave\nCompany-paid Life Insurance \nVoluntary supplemental life insurance \nShort and long-term disability insurance \nFlexible Spending Account\nHealth Savings Account\nTuition Reimbursement \nAbility to Participate in Employee Stock Purchase Program (ESPP)\nMental Wellness Benefits through Spring Health \nFamily-Forming support provided by Carrot\nPaid Parental Leave \nFlexible, full-service childcare support with Kinside\n401(k) with a generous employer match\nFlexible PTO\nCatered lunch each day in our office and data center locations\nA casual work environment\nA work culture focused on innovative disruption\nCalifornia Applicants\nCalifornia Consumer Privacy Act \nEqual Opportunity & Accommodations\nCoreWeave 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.\nAs 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: .\nExport Control Compliance\nThis 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. 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