Job Description

Founding AI/ML Engineer (Personalisation)

Location: SF Bay Area (hybrid) or In-Person ( Preferred ) 

Office : 505 Howard Street

Vision

We’re building the reasoning layer for customer experience, a privacy-first, explainable system that understands why each person buys, safely simulates outcomes, and orchestrates touch points in real time at near-zero marginal cost. It plugs into any stack as a vendor-agnostic brain and compounds into an autonomous orchestrator that maximises LTV and margin.

We already have distribution in motion via enterprise CX Partners, top-agency advisors (WPP, OMC), and committed angels.

What you’ll do

• Own the ranking/reco core: features, training, eval, online inference.
• Design cost-aware intelligence (keep the “why” without an LLM per event).
• Build offline generation + fast online serving/caching; handle cold-start/backfills.
• Ship with design partners; instrument and prove lift quickly.

Requirements

• Contextual data capture & featurisation (surveys/free text → embeddings/NLP).
• Cost-aware pre-computation; avoid per-event LLM; cadence-based refresh.
• Customer clustering & query taxonomy; catalog-aware resolution limits.
• Offline/online architecture; fast (profile/query) lookups; cache coherency.
• Cold-start strategies, backfills, and refresh triggers on new cohorts/catalog updates.
• Content personalisation for key discovery/conversion surfaces; multi-channel outputs.
• Profile update logic after actions; consistent state across caches/stores.
• 3–5 yrs ML with shipped impact in personalisation/CX (Klaviyo/Braze/Dynamic Yield/Uber/DoorDash-like).
• Stack: Python, PyTorch, SQL; Spark/Beam, Airflow, Kafka/Kinesis, Redis; GCP/AWS. Feast/Tecton a plus.
• Grit: bias to ship, founder hours, comfort with ambiguity.

Nice to have

E-com/subscription, privacy-by-design, RL/bandits/causal, simulation/synthetic personas.

Comp & setup

Meaningful founding equity + salary. Part-time → full-time post-raise or full-time now.

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