Scale is looking to drive the end-to-end execution of the Applied ML team’s initiatives, ensuring data quality, contributor marketplace efficiency, and extending horizontal ML capabilities across business units. The role will own program delivery from scoping to measurable adoption, serving as the communication backbone across engineering, product, and leadership to achieve measurable outcomes like higher data quality, faster contributor onboarding, and adoption of horizontal ML capabilities across BUs. The role will also manage the entire lifecycle of GenAI initiatives, from scoping to production deployment of LLM-based techniques, requiring a deep understanding of ML engineering processes, risk management, and rapid iteration in a demanding AI environment.
Requirements
- Familiarity with the Generative AI lifecycle, including the application of LLMs (Large Language Models) for structured downstream tasks, model fine-tuning, and performance evaluation.
- Ability to reason about system architecture, data flow, and key technical challenges in building scalable ML services in cloud environments (e.g., using AWS, GCP, or similar microservice infrastructure).
- Hands-on familiarity with high-performance compute infrastructure for ML training and deployment in real-world products.
- Familiarity with data quality pipelines or internal evaluation frameworks
- Strong software engineering fundamentals, ideally having prior experience as an engineer or developer before transitioning into program management.
- Experience driving adoption of technical platforms across multiple product lines or business units.
Responsibilities
- Own the end-to-end planning, scheduling, and execution for multiple GenAI ML programs, ensuring high-quality, on-time delivery against product goals.
- Collaborate closely with Product Managers and ML Engineers/Scientists to translate vision into clear, iterative execution plans.
- Ensure our ML systems move from prototype to production smoothly, by aligning engineering, research, and product teams around clear milestones, risks, and delivery standards.
- Translate technical complexity into concise, actionable updates for executive and cross-functional stakeholders.
- Proactively identify, track, and mitigate technical risks unique to GenAI development (e.g., model drift, prompt engineering evolution, data security/privacy, resource contention on large-scale compute).
- Manage dependencies and communication across core teams (e.g., Platform Infrastructure, Data Engineering, Product) to seamlessly integrate ML services into production microservice architectures.
- Provide clear, concise, and structured status reports to all stakeholders, including executive leadership, detailing project health, roadblocks, and delivery forecasts.
Other
- 5+ years of experience as a Technical Program Manager or similar technical leadership role managing complex software or machine learning development projects.
- 2+ years of experience managing programs specifically focused on Applied Machine Learning, MLOps, or Data Science.
- Experience with Agile methods and tools (e.g., Jira, Linear).
- Excellent verbal and written communication skills with a proven ability to translate complex technical challenges into clear program risks and business impacts for executive audiences.
- Interest in learning and engaging with modern ML/GenAI practices.
