Level

CoreWeave

Senior Software Engineer - AI Infrastructure Performance Insights & Observability

CoreWeave is hiring a Senior Software Engineer - AI Infrastructure Performance Insights & Observability in Sunnyvale and Bellevue, United States. It pays $182k-$242k a year and Level rates it ; you can apply on CoreWeave.

Level's read on this job

  • Level rates this software engineer role Builds AI ●●●●: the job is building AI systems.
  • Pay on this posting starts 8% higher than the typical software engineer posting on Level.
  • The posting mentions vLLM, PyTorch and Promql among its tools and skills.
  • Level classes it as a senior role.
  • CoreWeave has 317 other open roles on Level.

AI in this role

Senior engineer to build performance insights and observability systems for CoreWeave's AI infrastructure and GPU workloads.

vllmpytorchpromqlmetricsql
ai-infrastructureobservabilityperformance-tuningtime-series-databasesgpu-monitoring
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 this role

We're looking for a Senior Engineer to be a driving force on CoreWeave's Benchmarking & Performance team, with a focus on building the performance insights and observability systems that make our AI infrastructure legible at every layer, from individual GPUs and NVLink/InfiniBand fabrics up through distributed training and inference workloads. You will own how we detect, diagnose, and surface performance signals across every data center in our global infrastructure, turning billions of raw telemetry events into real-time insight that engineers, product teams, and executives can act on with confidence.

This is not a straightforward data engineering or BI role. The role will focus on building the observability and insight tooling that lets us answer, in near real time, whether a GPU fleet, a fabric, or a training run is performing the way it should, and why it isn't when it's not. If you're energized by building the systems that turn raw infrastructure telemetry into trusted, actionable performance intelligence, and you want that work to sit closer to the hardware and the workload than to a dashboard, this role was built for you.

What you'll do

  • Performance Insights & Observability - Design and build the systems that continuously assess AI infrastructure health and performance: GPU utilization and efficiency, interconnect (NVLink, InfiniBand, RoCE) fabric behavior, distributed training and inference throughput, and hardware degradation signals. Build the detection and diagnosis logic that surfaces anomalies and regressions before they become incidents, not just dashboards that report on them after the fact.
  • Time-Series & Metrics Infrastructure - Own and extend our time-series database (TSDB) layer as the backbone of real-time observability. Write and optimize PromQL/MetricsQL queries that power alerting, anomaly detection, and trend analysis across thousands of GPUs and hundreds of benchmark runs. Bridge streaming metrics and batch-analytical workloads so engineers get sub-second answers during live incidents and analysts get complete historical context for root cause work.
  • Fabric & GPU Telemetry - Build and validate the pipelines and metrics that make network fabric and GPU-level behavior observable and comparable across racks, clusters, and hardware generations, including gray failure detection, congestion and error-rate signals, and health scoring that holds up under audit.
  • Data Lake Architecture (in support of insight work) - Design and build the performance data lake that underpins the above: table formats (Apache Iceberg, Parquet, Avro), hot/cold tiering, and schema evolution for latency distributions, throughput metrics, GPU utilization, cost-per-token, and hardware health signals. This is foundational infrastructure, not the end product.
  • Query Optimization & Performance - Profile and tune query engines against columnar and time-series stores so that the observability layer meets its own strict P99 latency and freshness SLAs. Benchmark the benchmarking infrastructure itself.
  • BI & Reporting (secondary) - Where needed, build self-service views (Grafana, Looker, or similar) for engineers, product managers, and executives, but as a downstream output of the insight and observability work above, not the primary deliverable.

Who you are

  • 5+ years of experience building distributed systems, observability platforms, or performance engineering tooling, ideally for infrastructure or ML systems rather than general-purpose BI.
  • Strong coding in Python or Go (C++ a plus) and deep familiarity with networked systems, GPU infrastructure, and performance analysis.
  • Hands-on experience with Kubernetes at production scale, CI/CD, and observability stacks (Prometheus, Grafana, OpenTelemetry) used to monitor and diagnose infrastructure, not just report on it.
  • Working knowledge of time-series databases and fluency in PromQL or MetricsQL for building real-time alerting and anomaly detection, not only historical dashboards.
  • Familiarity with data lake architectures and modern table formats (Iceberg, Parquet, Avro) sufficient to support an insights platform, though this is not the primary skill this role is hiring for.
  • Comfortable working close to hardware and workload behavior: GPU utilization patterns, interconnect fabric health, distributed training/inference performance characteristics.
  • Strong communicator comfortable collaborating with cross-functional teams and external partners.

Nice to have

  • Experience with time-series databases, LSM-based storage engines, or custom telemetry pipelines.
  • Experience running MLPerf submissions or similar large-scale audited benchmarks.
  • Contributions to OSS projects such as Apache Iceberg, Apache Spark, Trino, llm-d, vLLM, or PyTorch.
  • Direct experience benchmarking or monitoring large GPU fleets or multi-region clusters.
  • Experience with CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies.
  • Familiarity with data cataloging, lineage tools, or data governance frameworks.

Why CoreWeave?

Own the data that tells the truth about performance. You'll build the analytical foundations the industry relies on to measure—and achieve—state-of-the-art results, working hand-in-hand with world-class partners and communities. If you love turning vast streams of telemetry into clear, trusted insights that move markets and drive engineering excellence, we'd love to talk.

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 provides the opportunity to develop innovative solutions to complex problems. As we get set for take off, the growth opportunities within the organization 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 $182,000 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.

How we rate this

Senior Software Engineer - AI Infrastructure Performance Insights & Observability at CoreWeave rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI InfrastructureObservabilityPerformance TuningTime Series DatabasesGPU MonitoringvLLMPyTorchPromql

Also common in software engineer jobs at this level

Pay

This job starts at
$182k
Typical software engineer job
$168k

This posting starts 8% above the typical software engineer job. See software engineer jobs

Questions you could be asked

  1. Tell me about a project where ai infrastructure was part of your work. What did you do?
  2. Tell me about a project where observability was part of your work. What did you do?
  3. Tell me about a project where performance tuning was part of your work. What did you do?
  4. Tell me about a project where time series databases was part of your work. What did you do?
  5. Tell me about a project where gpu monitoring was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI Infrastructure, Observability, Performance Tuning, Time Series Databases, and GPU Monitoring. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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