Scale ai

Overview

As Solutions Engineering Lead for Consumer, you'll be a player-coach: managing and growing a team of 3 to 5 Solutions Engineers while personally owning our most strategic consumer accounts.

About Scale ai

Scale plays a vital role in the development of AI applications, and consumer businesses are where those applications meet the most users, the most traffic, and the least patience for a bad experience.

Requirements & Eligibility

• A strong engineering background with significant experience in a customer-facing technical role (solutions engineering, solutions architecture, forward-deployed engineering, or technical consulting), including hands-on development in Python or simila
• Experience leading or mentoring technical individual contributors. Formal people management is welcome, but a credible track record as a tech lead or team lead matters more than title.
• Experience with high-scale consumer-facing systems such as recommendation and personalization, search, content moderation, or conversational products, and real fluency in the performance and cost constraints that come with them.
• Presentation skills with high technical credibility in front of both product or executive stakeholders and front-line engineers, plus the intellectual curiosity, empathy, and velocity to operate in a fast-moving vertical.

Key Responsibilities

• Manage, coach, and grow a team of 3 to 5 Solutions Engineers, setting expectations, running deal reviews, developing careers, and hiring as the vertical scales.
• Personally own the technical win on our most strategic consumer accounts, staying hands-on in demos, prototypes, and architecture conversations rather than managing from a distance.
• Build the vertical's technical playbook: reference architectures, demo environments, evaluation frameworks, and SOW patterns your team and the broader GTM org can reuse.
• Serve as the domain authority on GenAI at consumer scale, including agentic customer experience, personalization and search relevance, trust and safety and content moderation, and the latency, throughput, and cost-per-inference tradeoffs that come wi
• Partner with AEs and GTM leadership on account strategy, technical qualification, and pilot scoping that converts into production deployments.
• Work with forward-deployed Software and Machine Learning Engineers to carry solutions from initial design through early implementation.
• Translate patterns across your accounts into structured, prioritized input for Product and Engineering, and influence the roadmap on behalf of the vertical.