Senior Account Solutions Architect
Posted Updated
Description
- 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
- 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
- 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
- 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
- Demonstrated ability to break down and solve complex, often novel, technical problems with enterprise customers
- Proficiency in Python
- Experience designing and deploying production LLM-powered applications for customer use cases
- Excellent written and verbal communication and presentation skills, with the ability to translate technical concepts for both engineering and executive audiences
- 4+ years of relevant experience in a solutions engineer, AI-oriented solutions consultant, or technical field engineering role
- Hands-on experience training, fine-tuning, evaluating, and deploying deep learning models, including modern LLM architectures
- Familiarity with running AI workloads least one major cloud platform (AWS, GCP, or Azure)
- You’d rather show than tell — you build the demo or PoC instead of just pitching it
- You get excited by empty whiteboards —new customers, novel problems, new playbooks for AI natives
- You’re genuinely curious about your customers’ work — what they’re building, what’s getting in their way, what success looks like for them
- 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
- 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
- Familiarity one or more deep learning frameworks (PyTorch) and modern LLM stack (VLLM, langchain / LlamaIndex)
- Prior experience in a technical pre-sales or solutions architecture role focused on net-new logos or greenfield accounts
- 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)
- Working knowledge of cloud infrastructure for AI workloads, including GPU compute, high-performance networking, and storage
- Experience using Slurm or Kubernetes for ML job orchestration
- Background in ML Engineering, AI Engineering, MLOps, or LLMOps