Higgsfield AI — fastest-scaling generative AI company, $500M annual revenue run rate, 25M+ users, 6M+ generations per day.
Responsibilities
Build ranking systems for content surfaces: effects and presets, templates, models, prompts, and what to show a user next after a generation completes.
Solve cold start for new users with one onboarding signal and no history.
Handle position bias, popularity feedback loops, exploration vs exploitation.
Rank against the objective of a completed, kept, shared generation, not the click.
Own incrementality on offers: discounts, credit vouchers, trials, plan upgrade prompts, win-back campaigns.
Model uplift, not propensity.
Design randomized holdouts for uplift training and measurement.
Respect guardrails: margin and abuse signals as constraints on the objective.
Work with Legal on personalization rules.
Build churn and downgrade prediction for subscribers, repeat-purchase propensity for credit-pack buyers.
Watch for leakage in churn models.
Pair every propensity model with an intervention and an experiment.
Turn user generations into features: use-case and intent labels over prompts, input assets, output assets, model and parameters.
Mine failed, abandoned and refunded generations.
Own models end to end: problem framing, features, training, offline evaluation, serving, monitoring, retraining.
Ship behind experiments; monitor for drift and degradation.
Requirements
Experience shipping ML that served live user traffic and changed a business metric.
Depth in at least two of: recommender systems / learning-to-rank, uplift & causal ML, churn or propensity modelling, real-time personalization.