AI Trial-to-Paid Conversion Optimization Engineer

Fynix Studio

Remote · $160k – $200k / year · Full-time · Posted 4 months ago

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About the role

We closed our Series B two quarters ago and the pressure is on conversion. Our trial-to-paid rate is 18%. Our best competitors are at 26-31%. Closing that gap is worth roughly $8M in annual recurring revenue at our current trial volume, and our growth board has made it the top commercial priority for the next four quarters. You'll own the ML systems that drive conversion optimization: building the in-trial engagement models that predict which users are on a trajectory toward conversion or churn, designing the intervention framework that triggers the right action at the right moment (feature unlock, sales outreach, targeted tutorial, discount offer), and running the experimentation infrastructure that measures intervention lift without contaminating the trial experience. This is a senior IC role with direct commercial impact. You'll be embedded in the growth pod alongside a product manager, a growth engineer, and a data analyst. Your model decisions translate directly into revenue outcomes that are measured and reported monthly.

Fully remote.

Responsibilities

Build in-trial user engagement models predicting conversion probability and trajectory
Design intervention framework triggering personalized actions based on behavioral signals
Develop experimentation infrastructure for clean measurement of intervention lift
Create segment-level conversion analysis identifying highest-leverage user cohorts
Build lead scoring for sales team prioritization among high-engagement trial users
Monitor model performance and run monthly retraining as trial cohort composition shifts

Requirements

6+ years in ML or data science with SaaS growth or conversion optimization focus
Experience with in-product behavioral modeling and intervention design
Deep A/B testing and causal inference skills
Strong Python, SQL, and distributed computing skills
Track record of measurable revenue impact from ML-driven growth initiatives