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AdZeta · 1 day ago

Founding ML Engineer (Ads & Signal Engineering) - NYC/ SF Preferred

New York, NY

Full-time

Hybrid

Senior Level

AdZeta is building a signal engineering and activation layer for modern growth teams, aiming to unify first-party data and predict customer lifetime value (LTV). The Founding ML Engineer will be responsible for designing and shipping LTV prediction systems, building modeling approaches tailored to adtech realities, and owning MLOps processes.

Computer Software

Hiring Manager

Abhi Agnihotri

Responsibilities

Design + ship LTV prediction systems end-to-end: data → features → training → evaluation → deployment → monitoring

Build modeling approaches that work under adtech realities: delayed conversions, sparse signals, noisy attribution, changing platform policies

Own MLOps: reproducible training, CI/CD for models, logging/monitoring, data quality checks, drift + performance alerting

Partner with product + GTM to translate business outcomes (profit, payback, repeat rate) into model objectives and activation strategies

Help define our 'Signal Engineering' playbook: what we compute, how often, and how we send it

Qualification

LTV modelingProduction MLPythonSQLMLOpsAdtech experienceStreaming systemsEvent pipelinesDockerGCPScikit-learnAirflow

Required

Strong ML foundations + applied instincts (you can reason from objective → data limitations → model choice → deployment)

Hands-on experience building production ML (training pipelines, deployment, monitoring; not just notebooks)

Experience with LTV modeling (e.g., probabilistic/BTYD-style thinking, survival/retention modeling, regression/classification for value, calibration)

Excellent Python + SQL; comfortable in modern data stacks and cloud environments

Preferred

Adtech/Martech experience: Meta CAPI, Google Ads/Enhanced Conversions/Offline Conversions, audience/CRM activation, conversion quality, incrementality intuition

Identity/data joining experience (hashed PII, multi-key matching, deduping, event stitching)

Experience with streaming/near-real-time systems and event pipelines

Familiarity with experimentation frameworks (uplift/incrementality), MMM/attribution constraints, or measurement-heavy environments