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Account Solutions Architect (Engaged)

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Onsite

Account Solutions Architect (Engaged)

CoreWeave

Account Solutions Architect (Engaged)

Skills

Google Cloud PlatformDeep LearningPythonPyTorchMachine LearningAIAWSKubernetesAzure

2 months ago

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Important information


Contract type:

Permanent

Salary:

$182,000 – $242,000 / year

Location:

Austin, TX, USA

Work mode:

Onsite

Published on:

29 August 2026

What they need


  • CoreWeave is the AI-first cloud where every layer, from compute and networking through orchestration, observability, and the broader AI tooling stack, is purpose-built for AI workloads
  • The Field Engineering team owns the technical relationship with every CoreWeave customer
  • We partner with Sales, Support, Product, and Engineering to deliver technical success across the full customer lifecycle
  • You’ll work hands-on with some of the most advanced AI teams in the world as they build, train, deploy, and scale their workflows
  • Greenfield is where we meet the next generation of those teams — net-new customers that haven’t yet built on CoreWeave
  • We hire technical, AI Solution Architects who want to operate the full stack, own customer relationships end-to-end, and grow on a visible technical ladder that recognizes customer impact above all
  • The Account Solutions Architect, Engaged is the named technical partner to existing customers across CoreWeave’s full platform — infrastructure, Models, Weave, observability, and inference
  • You’ll help AI teams solve real-world problems by deepening platform adoption, identifying and driving expansion opportunities, strengthening relationships with key technical stakeholders, and serving as a trusted advisor as customers scale their AI workloads in production
  • You’ll partner closely with Account Managers on the commercial motion, with Specialist Field Engineers for deep domain expertise, and with customer teams to ensure they are getting maximum value from CoreWeave
  • You’ll represent the voice of the customer internally, surface product
  • Feedback from the field, and proactively address technical blockers and business-critical needs to ensure customer success
  • This role is for customer-focused AI practitioners who are excited to help leading AI companies deepen their partnership with CoreWeave and scale the next generation of AI workloads in production- Hands-on experience training, fine-tuning, evaluating, and deploying deep learning models, including modern LLM architectures
  • Demonstrated ability to break down and solve complex, often novel, technical problems with enterprise customers
  • Experience designing and deploying production LLM-powered applications for customer use cases
  • Proficiency in Python
  • Excellent written and verbal communication and presentation skills, with the ability to translate technical concepts for both engineering and executive audiences
  • Familiarity with running AI workloads least one major cloud platform (AWS, GCP, or Azure)
  • 4+ years of relevant experience in a solutions engineer, AI-oriented solutions consultant, or technical field engineering role
  • Wondering if you’re a good fit? We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren’t a 100% skill or experience match. Here are a few qualities we’ve found compatible with our team. If some of this describes you, we’d love to talk
  • Prior experience in a technical pre-sales or solutions architecture role focused on net-new logos or greenfield accounts
  • Experience using Slurm or Kubernetes for ML job orchestration
  • Experience with hyperparameter optimization and experiment tracking tools\
  • Familiarity with high-performance GPU infrastructure (e.g., NVIDIA H100/H200/B200, InfiniBand networking, parallel file systems)
  • Background in ML Engineering, AI Engineering, MLOps, or LLMOps
  • Working knowledge of cloud infrastructure for AI workloads, including GPU compute, high-performance networking, and storage
  • Familiarity one or more deep learning frameworks (PyTorch) and modern LLM stack (VLLM, langchain / LlamaIndex)

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