Currently: ML Engineer @ Meta. Goal: $1M+ equity that could 100x, ideally on AI-for-investing. Filter: company has Seattle presence OR is remote-OK OR is realistically negotiable to Seattle for a senior hire.
Honest tension up front: Seattle's strong AI presence is dominated by mega-caps (Microsoft AI, Amazon/AWS, Meta Bellevue, Anthropic, OpenAI's new office). Those score high on Learn and Fit but low on the 100x equity bet — their equity is RSUs in already-massive public companies. The pure 100x bets (small frontier labs, hardware startups, robotics) almost all require Bay Area. So if Seattle is hard, the realistic path is "high learning + cash comp now, lottery equity later via a spinout." If Seattle is truly soft, you should be willing to spend 6-12 months in the Bay first.
Scores 1–5. 100x = chance equity 100x's. Learn = research/learning value. InvAlign = AI-for-investing alignment. Fit = realistic chance you land it. Priority = weighted composite (100x×2 + InvAlign×1.5 + Learn + Fit) — reflects your stated priorities of equity outcome & investing mission first, learning & fit secondary. Tier: A = Seattle office where the AI work happens, B = remote-OK formally, C = would require you to negotiate Seattle as a condition (low probability).