Top Tech Jobs & Startup Jobs

Leads product design for Scale’s enterprise AI data business, covering 0-to-1 initiatives and established products. Designs user flows, wireframes, mockups, prototypes, dashboards, data visualizations, editors, and forms for desktop web applications. Partners with product, engineering, data science, and operations teams; advocates for users; contributes to the design system; identifies product gaps; and drives solutions in a fast-paced, ambiguous environment.

Lead and coach a small Frontier AI Infrastructure engineering team while remaining hands-on with coding. Own the model inference layer, including scalable backend systems, networking, latency, usage metrics, integrations, incident response, and operational health. Set technical direction, manage delivery commitments, engage cloud vendors and government customers, support secure cloud-native deployments, and build engineering standards for testing, code review, on-call, and compliance.

Manage and grow a large global contributor community for Scale’s Generative AI business. Develop engagement, retention, communications, feedback, and dispute-resolution strategies; oversee community platforms, virtual events, sentiment monitoring, and contributor messaging. Use metrics and feedback to improve operations and product strategy, coordinate cross-functional stakeholders, remove blockers, and maintain cohesive communications. The role also includes managing at least one direct report and executing operational initiatives directly.

Software Engineering Interns will build and ship production software for AI systems, including data pipelines, evaluation infrastructure, agentic applications, fraud detection, matching algorithms, contributor tools, and AI infrastructure products. Interns own roadmap work with mentorship and collaborate on scalable, high-quality products. The role requires product engineering experience, prior software engineering internship experience, large-scale data systems experience, and familiarity with Python, TypeScript, React, and/or MongoDB.

Build and ship customer-facing software products and AI applications, including data pipelines, evaluation infrastructure, agentic systems, fraud detection, matching algorithms, contributor tools, and RAG applications. Collaborate directly with customers, process large-scale data, integrate APIs and services, and iterate quickly on high-quality products.

As an Engagement Manager, you'll manage relationships with national security customers, leading project teams to exceed AI/ML objectives, while ensuring exceptional customer service and successful account onboarding.

Build scalable analytics solutions for the Public Sector business, including dashboards, data models, tables, metric definitions, and ad hoc analyses. Own data quality, partner with Operations and Finance on efficiency and cost analysis, and translate complex findings into actionable insights. Drive cross-functional projects from ambiguity to completion while supporting planning, pricing, and business decision-making.

Own the technical foundation for building, packaging, executing, verifying, and scaling reinforcement learning environments. Design sandboxed execution, rollout orchestration, trajectory capture, verifier frameworks, environment versioning, and authoring tools. Build high-throughput infrastructure and reliable reward signals, instrument real applications, develop task suites, and defend against reward hacking. Provide staff-level technical leadership across engineering and research teams while shipping complex production systems.

Product Manager for Gen AI Platform to drive strategy, execute product roadmaps, and manage cross-functional teams while ensuring quality training data systems for AI models.

Research and develop novel post-training methods such as SFT, RLHF, reward modeling, and preference optimization for text and multimodal LLMs. Analyze model behavior, improve alignment and generalization, mitigate bias, enhance robustness, and publish findings at leading AI conferences. Collaborate with engineers, researchers, and foundation model labs to establish data-driven AI development practices and provide technical and strategic guidance.