codax
Founding AI/ML Engineer ( Personalisation )
codax, San Francisco, California, United States, 94199
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.
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.