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Los Angeles, California, United States
Remote only
You are a technical product manager who owns what a model optimises for, not just the roadmap around it. You have real depth in recommendation and ranking systems, and you will own the recommendation engine behind the Xsolla Advertising Products: the ranking that decides which offers millions of players see, and in what order.
You hold the bar high for how decisions get made. You can read a readout and say plainly what it does and does not support. You connect model performance to revenue without overclaiming, and you bring a low-ego, people-first approach to work that spans ML, analytics, ads operations, and commercial teams.
If you are excited about using ML to build building the next generation of advertising, rewards and loyalty programs for mobile apps and games we want to hear from you.
Xsolla is a global commerce company with robust tools and services to help developers solve the inherent challenges of the video game industry. From indie to AAA, companies partner with Xsolla to help them fund, distribute, market, and monetize their games. Grounded in the belief in the future of video games, Xsolla is resolute in the mission to bring opportunities together, and continually make new resources available to creators. Headquartered and incorporated in Los Angeles, California, Xsolla operates as the merchant of record and has helped over 1,500+ game developers to reach more players and grow their businesses around the world. With more paths to profits and ways to win, developers have all the things needed to enjoy the game.
For more information, visit xsolla.com.
Own the recommendation engine as a product: ranking, relevance, and the model roadmap. Ranking quality is the product.
Set the objective the ranker optimises for, the constraints it works inside, and the guardrails and kill conditions that bound it. The target is eCPM today; what comes next is your call.
Own the metric definitions the whole team measures against, and settle the definitional questions that change the answer: gross against publisher-side RPU, app-load against impression-user denominators, mean of daily values against window totals.
Collaborate with on ML frameworks, experiment design, hold-outs, including pre-registered decision rules, power calculations, sample-ratio-mismatch checks, A/A validation, multiplicity correction, and named rollback triggers.
Collaborate with DS on readouts to evaluate model performace and readouts to business stakeholders readout: what the model did, what it cost, and what happens next.
Contribute the ranking perspective to the wider ads roadmap, including the seams where pricing, identity, and event telemetry meet ranking.
6+ years in product management, with 3+ years owning a recommendation, ranking, personalization, or relevance system in production.
Deep understanding of experiment design and inference, including minimum detectable effect, statistical power, sample ratio mismatch, A/A testing, multiple-comparison correction, and pre-registration. You can tell when a readout will not support the claim being made from it, and you say so.
Working knowledge of the modelling itself: classification and regression, probability calibration, predicted conversion rate, expected-value objectives, and how the choice of objective changes model behavior.
Hands-on experience with ML and data platforms such as Snowflake, BigQuery, Spark, Airflow, dbt, MLFlow, Vertex AI, and feature stores.
Proven record of moving model changes into production traffic through a controlled experiment process, rather than shipping on a dashboard reading.
Knowledge of performance advertising economics and ad tech ecosystems, including eCPM, RPU, bid multipliers, campaign hierarchies, and attribution models.
SQL you write yourself, and enough comfort reading application code to verify a claim instead of taking it on trust.
Excellent written communication and stakeholder management across technical and non-technical audiences. Specs, decision records, and readouts are the output of this role, and other teams act on them without you in the room.
Bachelor's or Master's in Computer Science, Engineering, Statistics, Economics, or a related field.
Offerwall, rewarded advertising, loyalty and rewards programs and/or mobile app monetization experience is a plus.
Experience with a two-stage ranking system, where an external service supplies the base order and local logic adjusts it, is a plus.
Pricing, yield management, or marketplace experience alongside ranking is a plus.
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