Data Science, Product ML Engineer

📋 Job Overview

Higgsfield AI is the fastest-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.

🏢 About Higgsfield AI

Why work at Higgsfield AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like.

🎯 The Role

Right now every user sees roughly the same Higgsfield. The same effects in the same order, the same paywall, the same offer, the same lifecycle emails - across 25M+ people who want completely different things from us. This role makes the product decide per user. You own the models behind that: what we recommend, who gets which offer, who we spend retention effort on, and what each of those is worth. Not a research agenda - production models on live traffic, measured in revenue, retention and margin.

✅ Key Responsibilities

• Build the ranking systems for our content surfaces: effects and presets, templates, models, prompts, and what we show a user next after a generation completes.
• Solve cold start properly - a brand-new user with one onboarding signal and no history is our single most common case, not an edge case.
• Handle the things that make recsys hard in the real world: position bias, popularity feedback loops, exploration vs exploitation, and the fact that a ranker trained on logged behaviour learns to reproduce the ranking it was trained on.
• Rank against the objective we actually want - 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. Targeting the users most likely to convert spends margin on people who would have converted anyway; the entire value of this work is finding the users whose decision the offer changes.
• Design the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently.
• Respect the guardrails: an offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup.
• Work with Legal on what may be personalized. Personalized pricing and discounting touches consumer-protection and consent rules in several of our markets - you should want that conversation, not route around it.
• Build churn and downgrade prediction for subscribers, and repeat-purchase propensity for credit-pack buyers.
• Know the difference between a model that predicts churn and a model that reduces it. A high AUC earned by detecting users who already stopped using the product is worth nothing.
• Watch for leakage relentlessly - cancellation-adjacent features will hand you a beautiful offline number and a useless system.
• Pair every propensity model with an intervention and an experiment. The deliverable is a retained user, not a score.
• Turn what users actually make into features the models above can use: use-case and intent labels over prompts, input assets, output assets, model and parameters.
• Content understanding here is instrumental — a taxonomy exists so ranking knows what a preset is for and so an offer knows which use case a user is stuck on. Multimodal, because a large share of our generations carry almost no prompt text and the intent lives in the uploaded image.
• Mine failed, abandoned and refunded generations: they are the strongest churn and unmet-demand signal we have, and success-only training data has survivorship bias built in.
• Own models end to end: problem framing, features, training, offline evaluation, serving, monitoring, retraining.
• Ship behind experiments. Offline metrics decide what to try; online experiments decide what stays.
• Monitor for drift and degradation - new model launches, pricing changes and seasonality all move the ground under a deployed model.
• Write down what each model is worth, in money, and keep that number current.

📌 Required Qualifications

• Experience shipping ML that served live user traffic and changed a business metric. Notebooks and offline benchmarks are not this.
• Depth in at least two of: recommender systems / learning-to-rank, uplift & causal ML, churn or propensity modelling, real-time personalization.
• Genuine causal literacy: randomized holdouts, incrementality, Qini/uplift evaluation, selection effects, why a lift measured before-and-after is usually not a lift.
• Strong Python and SQL; comfortable with gradient boosting and neural ranking, and with the boring parts - feature pipelines, training/serving skew, latency budgets, retraining cadence.
• Product judgment: you can name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model.
• Comfort with images and video as data, or clear appetite to get there fast.
• Pragmatism. A heuristic shipped this month that lifts conversion beats a two-quarter platform. Then you replace the heuristic.
• Clear written and spoken English, B2+.

⭐ Desirable Experience

Backgrounds that often do well in this role include:

• Machine learning engineering or data science with a focus on production systems
• Experience with large-scale personalization platforms
• Strong understanding of causal inference and experimentation
• Experience with multimodal data processing

🎁 Benefits

Competitive salary and benefits package.

🛂 Visa & Eligibility

Eligibility to work in Kazakhstan required.