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Overview
Join Scale AI as a Technical Program Manager to drive the development of the Scale Generative AI Platform. Collaborate with engineering teams to enhance infrastructure initiatives and ensure reliable, scalable systems. At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace.
Key Responsibilities
- •Lifecycle & Platform Delivery: Lead strategic planning and high-velocity execution for SGP core capabilities (orchestration layers, model serving, APIs). Manage features from technical scoping and architecture design through production launch.
- •Cross-Functional GenAI Alignment: Drive execution and manage complex technical dependencies across systems engineering, Core ML, Research, and Product teams to deliver unified SGP capabilities with architectural consistency.
- •Technical Translation & Requirements: Translate complex infrastructure metrics (LLM inference optimization, GPU utilization, compute orchestration) into actionable roadmaps. Map demands like multi-tenancy, data privacy, and isolation into platform features.
- •Risk & Dependency Mitigation: Proactively identify, track, and mitigate technical risks unique to massive-scale GenAI infrastructure and global SGP deployments, maintaining momentum despite fast-evolving AI frameworks.
- •Developer Velocity & Operational Excellence: Establish lightweight agile processes that empower engineers to ship fast without breaking core systems. Define and enforce clear SLOs and performance benchmarks to guarantee production-grade reliability for clients.
- •Metrics-Driven Adoption: Track and report on SGP adoption metrics, system reliability, delivery forecasts, and engineering bottlenecks directly to executive leadership to ensure the platform scales responsibly.
Minimum Qualifications
- •5+ years of experience as a Technical Program Manager, Product Manager, or Software Engineer, with a proven track record of having built and shipped technical products or platforms from scratch (e.g., internal cloud infrastructure, developer APIs, distributed systems, or ML platforms).
- •Platform Domain Expertise: 3+ years of dedicated experience managing programs focused directly on core engineering infrastructure, cloud-native ecosystems (AWS/GCP), container orchestration (Kubernetes), or distributed systems.
- •AI/ML Infrastructure Literacy: Foundational understanding of the infrastructure required for the Generative AI lifecycle, including high-throughput data pipelines, GPU/CPU cluster utilization, or model training/evaluation setups.
- •Masterful Communication: Proven track record of presenting to and influencing executive-level stakeholders, with the ability to translate complex technical/architectural challenges into clear business impacts.
- •Execution Excellence: Advanced proficiency with iterative development methodologies and modern project management tooling (Linear, Jira, etc.) applied to foundational infrastructure environments.
Nice-to-Have Qualifications
- •Engineering Roots: Strong software engineering fundamentals, with prior professional experience as a Software Engineer, DevOps Engineer, or Data Developer before transitioning into program management.
- •Platform Adoption Track Record: Proven success driving the internal adoption of technical platforms, SDKs, or APIs across disparate, fast-moving product lines.
- •Data-Centric AI Familiarity: Direct experience working with large-scale data quality pipelines, distributed vector databases, or specialized AI inference engines (e.g., Triton, Ray).