ml engineer for personalization and monetization

Higgsfield AI is a generative AI company focused on AI-powered video creation and next-generation creative tools. Its products serve more than 25 million users worldwide and support content generation for major global brands.

• Build ranking systems for effects, presets, templates, models, prompts, and post-generation recommendations;
• Solve cold-start personalization for new users with limited signals;
• Address position bias, popularity feedback loops, exploration versus exploitation, and logged-behavior bias;
• Optimize ranking for completed, retained, and shared generations rather than clicks;
• Own incrementality for discounts, vouchers, trials, upgrades, and win-back campaigns;
• Design and maintain randomized holdouts for uplift modeling and measurement;
• Apply margin and abuse guardrails to offer optimization;
• Work with Legal on personalization, pricing, discounting, consumer protection, and consent requirements;
• Build churn and downgrade prediction for subscribers and repeat-purchase propensity models for credit-pack buyers;
• Pair propensity models with interventions and experiments to improve retention;
• Monitor for data leakage in churn and retention models;
• Create use-case and intent features from prompts, input assets, output assets, models, and parameters;
• Analyze failed, abandoned, and refunded generations as churn and unmet-demand signals;
• Own models end to end from problem framing and feature development through training, evaluation, serving, monitoring, and retraining;
• Ship models behind experiments and use online results to determine what remains in production;
• Monitor model drift and degradation caused by launches, pricing changes, and seasonality;
• Track and maintain the business value of each model in monetary terms.

• Experience shipping machine learning models serving live user traffic and changing business metrics;
• Depth in at least two areas: recommender systems, learning-to-rank, uplift and causal ML, churn or propensity modeling, real-time personalization;
• Strong causal understanding of randomized holdouts, incrementality, Qini and uplift evaluation, selection effects, and before-and-after bias;
• Strong Python and SQL skills;
• Experience with gradient boosting, neural ranking, feature pipelines, training-serving skew, latency budgets, and retraining cadence;
• Product judgment to define the decision and metric before selecting a model;
• Comfort working with images and video as data, or willingness to learn quickly;
• Pragmatic approach to shipping heuristics and replacing them when appropriate;
• Clear written and spoken English at B2+ level;
• Not suitable for candidates who want to train or fine-tune generative video models, optimize offline metrics without deployment ownership, target users only by conversion propensity, rely on before-and-after lift comparisons, require clean labeled data and an existing feature store, or want predictable 9–5 workdays;
• Nice to have: Background in recommender systems or ranking for scaled consumer products, growth or monetization ML in subscription, gaming, fintech, or e-commerce, causal inference or uplift systems, applied science with production model ownership.

• Competitive base salary in USD, based on experience, skills, and role scope;
• Equity participation through the company’s stock option program;
• Relocation support to Almaty for candidates moving from another city or country;
• Collaborative, fast-paced environment with direct access to experienced leaders;
• Opportunities for professional growth, ownership, and career development;
• Company-provided equipment, meals, transportation, and other office benefits;
• Office work five days per week for the full working day.