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 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.

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.

Design, build, and maintain scalable, fault-tolerant serving platforms for LLMs. Integrate models for production and research, implement monitoring and observability, perform architecture reviews, and lead cross-functional end-to-end projects enabling LLM capability discovery and high-performance serving.

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.

Design, build, and operate a secure, high-performance sandboxing platform and client library for code execution across containerized and virtualized environments. Ensure strong isolation, optimize latency and resource use, debug and reduce errors, respond to incidents, and drive roadmap and architecture reviews while partnering with internal users.

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

Lead design and development of core data storage, streaming, caching, and indexing platforms. Collaborate with cross-functional teams to deliver features, improve practices, mentor engineers, and present technical guidance to stakeholders while ensuring platform reliability and scalability.

Lead the Forward Deployed Engineering team, overseeing AI deployment and production excellence for major enterprise accounts while enhancing team capabilities and organizational standards.

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.

Lead and own EMEA-specific assurance and certification programs (GCC, UK, EU), map controls, collect evidence, manage gaps and auditors, extend global certifications (SOC2, ISO), support customer assurance, legal and procurement compliance, and coordinate audits and regional regulatory engagement.

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.