Building useful AI systems means working through fragmented data, existing infrastructure, and model behavior that can be difficult to predict. You’ll test what works, understand failure cases, and build software that people can depend on.
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An operating system for a private equity firm with $115 billion under management.
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AI verification of in-store sampling stations for a national retailer with 70,000 field staff.
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Camera systems across thousands of retail stores to detect theft, manage queues, and track inventory.
Our clients span finance, software, media, industrials, energy, logistics, professional sports, healthcare, consumer packaged goods, real estate, and market research. Each engagement brings different technical constraints and business requirements.
You’ll also work on problems in agent orchestration, evaluation, memory, and long-running execution, where established engineering patterns are still developing.