Workflow Engineer
Nscale
| Company | Nscale |
| Category | Engineering |
| Location | AMER |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 6 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (greenhouse) |
Description
About Nscale
Nscale is the GPU cloud engineered for AI. We provide high-performance, cost-effective infrastructure for AI start-ups and enterprise customers building at the frontier of artificial intelligence.
AI is reshaping how companies operate, how governments function, and how industries compete. Nscale exists to build the infrastructure foundation that makes that transformation possible — responsibly, efficiently, and at scale. Our GPU cloud helps customers reduce the complexity of AI development while improving performance, managing cost, accelerating innovation, and supporting more sustainable infrastructure choices.
We are building a culture defined by ownership, accountability, openness, and pace. As an Nscaler, you will be expected to take pride in your craft, move with urgency, build trust through transparency, and contribute to the technology powering the next generation of AI.
About the Role
We are hiring a Workflow Engineer to design, build, and own the data infrastructure, applications, and workflows that power Nscale’s operating model.
This is a senior, hands-on role with significant influence across Infrastructure, Product, Finance, Commercial, People, Operations, and executive leadership. You will define the enterprise data and workflow roadmap, architect solutions in Palantir Foundry, and ensure teams across the business — from the C-suite to the datacenter floor — can make decisions from accurate, governed, trusted data.
This is a rare opportunity to help shape the operating system of a high-growth AI infrastructure company. You will work at the intersection of strategy and execution: identifying the highest-value workflows, translating ambiguous business needs into scalable systems, and turning complex operational inputs into production-grade products that teams actually adopt.
What You’ll Be Doing
You will own Nscale’s enterprise data and application roadmap end to end — from use-case discovery and prioritisation through to architecture, implementation, rollout, and adoption.
You will design and implement standards for data ingestion, pipeline orchestration, semantic modelling, workflow automation, and governance across Nscale’s analytics and operational systems.
You will work directly with business and technical stakeholders to understand current processes, gather requirements, document workflows, validate assumptions, and translate business needs into durable technical solutions.
You will manage canonical data models, ontologies, RBAC, access frameworks, and governance patterns that enable reliable, secure, self-service use of data across the company.
You will integrate operational, infrastructure, commercial, financial, and AI systems into a unified data fabric, creating feedback loops that measure performance, reliability, adoption, and business impact.
You will drive platform adoption through onboarding, documentation, enablement, and self-service tooling that make complex systems simple for users.
You will contribute to Nscale’s internal Palantir Center of Excellence, including platform standards, reusable patterns, vendor engagement, and best-practice development.
About You
You have 5+ years of experience in data engineering, workflow engineering, analytics engineering, or enterprise application development in fast-moving environments.
You are proficient in Python and TypeScript, comfortable with Git-based development workflows, and able to build production-quality systems rather than one-off analyses.
You have hands-on experience building and deploying solutions in Palantir Foundry, including data pipelines, ontology-backed applications, workflow tooling, and governed data products.
You can turn ambiguous briefs into shipped, adopted products. You are comfortable finding structure in complexity, asking sharp questions, and reducing messy processes into clear workflows and scalable systems.
You are able to operate across all levels
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