The opportunity
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. Retail and e-commerce, media and streaming, gaming, marketplaces, and social…
What you'll do
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 with millions of users.
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.
What they're looking for
- Translate patterns across your accounts into structured, prioritized input: for Product and Engineering, and influence the roadmap on behalf of the vertical.
- 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 similar.
- 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.
