Jobs at Scale AI

As an Applied AI Engineer, you will build AI applications for public sector clients, create and evaluate datasets, and collaborate with teams to address challenges using AI solutions.

As a Software Engineer, you will develop backend services for AI applications, optimize cloud infrastructure, and manage production-level deployments.

The Machine Learning Engineer will design, train, deploy, and evaluate AI models to address public sector challenges, ensuring high-quality data training and evaluating systems.

Lead end-to-end product design for Scale’s enterprise AI data products, including insurance AI, document processing, dashboards, data visualizations, editors, and forms. Create user flows, wireframes, mockups, and prototypes; collaborate with product, engineering, data science, and operations teams; advocate for users; identify product gaps; and contribute to the company design system.

Lead and grow Scale AI’s enterprise product design team, set design direction, hire and develop designers, and drive execution with Product and Engineering. Establish research practices, maintain high craft standards, contribute to the shared design system, and define interaction patterns for enterprise users working with AI agents and human-in-the-loop systems.

Lead end-to-end 0-to-1 product design for AI-driven B2B experiences serving government and enterprise clients across the Middle East. Translate stakeholder and client insights into concepts, prototypes, and polished visual designs while collaborating with product managers and engineers. Use research, data, and user insights to guide decisions, manage expectations, and ship impactful products. The role requires Arabic and English fluency, GCC experience, and familiarity with AI-powered design tools.

Build and ship production software for AI systems, including data pipelines, evaluation infrastructure, agentic applications, fraud detection, matching algorithms, and AI tooling. Interns work on full-stack product engineering, large-scale data systems, and customer-focused iteration while receiving mentorship. The role requires prior software engineering internship experience, shipped products, and familiarity with Python, TypeScript, React, and/or MongoDB.

Build and ship customer-facing AI products and software systems from day one. Responsibilities include developing full-stack applications, data pipelines, evaluation infrastructure, agentic AI tooling, fraud detection, matching algorithms, contributor tools, AI infrastructure, and customer service applications. Engineers work directly with customers, iterate quickly, and deliver reliable products capable of operating at large scale.

Design and develop shared foundational platforms, scalable distributed systems, cloud infrastructure, orchestration, and developer tooling. Collaborate across teams to deliver iterative software features, improve engineering practices, and lead experimentation in AI-related technologies. Build retrieval and understanding systems, agent orchestration platforms, reliable data storage, and CI/CD workflows while communicating technical concepts to technical and non-technical stakeholders.

Build and evolve cloud-agnostic deployment and observability infrastructure across AWS, Azure, GCP, OCI, and on-premises environments. The role involves developing secure, reproducible deployment pipelines, improving platform observability, debugging production issues, responding to incidents, conducting root cause analysis, shaping the roadmap, leading architecture reviews, and owning infrastructure projects end to end.

Lead an infrastructure engineering team to design, deploy, and operate scalable, secure production environments for AI-driven applications. Define roadmap, maintain SLAs/SLOs, build CI/CD and IaC pipelines, manage on-prem and cloud Kubernetes deployments, improve developer tooling, and drive incident response and monitoring best practices while mentoring engineers.

Lead development of production AI behavior, governance, and MLOps standards for high-risk public-sector systems. Architect and validate safety-focused AI components, build reusable agent and evaluation capabilities, advise executive clients on AI strategy, resolve severe model-safety incidents, and coach senior engineers while shaping the technical roadmap and recruitment.

Build and scale an enterprise Generative AI platform. Own large product areas across backend and frontend, develop reliable Python backend services, integrate LLMs, vector databases, and AI infrastructure, work with distributed systems and data pipelines, and ship features quickly while collaborating with product, ML, and infrastructure teams.

Build and operate core retrieval and knowledge representation systems for a generative AI platform: design RAG pipelines, embeddings, indexing, vector-store integrations, knowledge graphs/ontologies, evaluation frameworks, and production ML backend services while collaborating across product, ML, and infra teams and shipping enterprise-grade features.

Build and run EMEA GTM onboarding, centralize and maintain a sales knowledge base, deliver recurring enablement programs, and support priority deals by creating reusable playbooks and field-ready materials to help sellers communicate Scale's AI capabilities.

Manage engineering teams to deliver AI-driven applications, oversee product lifecycle, mentor engineers, and collaborate cross-functionally.

The Staff Technical Product Manager will own the product roadmap for Global Defense, design AI products, collaborate with engineers, and understand defense workflows to drive product outcomes.

As a Machine Learning Engineer, you'll train and deploy ML models for enterprise clients, work on deep learning solutions, and interface with product teams to enhance AI applications.

As a Solutions Engineer, you'll engage with customers to design AI solutions, deliver demos, and collaborate with teams, ensuring technical needs are met.

Lead regional workplace operations across EMEA and MENA: run on-site facilities and life-safety, manage vendors, deliver capital build-outs, track leases and obligations, handle escalations, partner with cross-functional stakeholders, and own regional operating and capital budgets and space planning.