Games Factory Talents | Connecting Passion & Talent

AI Engineer

• Improve and optimize AI-driven systems (e.g., make them faster, more cost-efficient, and higher quality)
• Design, build, and iterate on AI agents
• Fine-tune and adapt open-source language models for specific use cases
• Deploy and manage models in production environments
• Work on backend systems that integrate AI models into a scalable product
• Collaborate closely with product and engineering to enhance core capabilities
• Experiment with the latest AI tools, frameworks, and workflows
• Contribute across the stack when needed in a small, high-impact team

Growth Marketing

• Source, onboard, and manage an offerwall/rewarded partner portfolio (tests, launches, caps, tiering, pacing).
• Build and optimize CPE goal funnels : milestone ladders, payout tiers, and performance guardrails.
• Run weekly partner scorecards and decisioning: scale, pause, renegotiate, or block.
• Protect quality: identify fraud/low-quality patterns (abnormal completion timing, device/source anomalies, chargebacks) and coordinate partner actions and blocks.
• Own Singular as your core operating tool: integrations/config, partner setup, event mapping, attribution windows, and postbacks.
• Build and maintain dashboards + reporting cadence (we have some internal tooling, but you’ll own the overall UA measurement workflow).

Senior UA Manager – Incentivized Growth, Offerwalls, Rewarded Networks

Account Manager

• Manage a portfolio of ~7 Chinese gaming advertisers initially (expected to grow; typical account capacity is ~10–12 partnerships)
• Lead end-to-end account management: kick-off → launch → ongoing optimization → growth/upsell
• Build strong relationships and trust with Chinese stakeholders through confident, structured Chinese-language communication
• Balance performance outcomes and quality requirements: align internal KPIs with advertiser-side quality expectations (e.g., traffic quality and performance metrics)
• Create and optimize multi-reward funnels: adjust payouts, refine reward structures, and adapt funnel structures to improve results
• Run recurring client calls (monthly / quarterly) and maintain consistent day-to-day communication
• Collaborate with internal teams (product/ops/performance) to resolve issues quickly and improve partner outcomes
• Identify upsell opportunities (additional budgets, additional titles) and drive scaling across existing partnerships

Account Manager, Native Chinese Speaker – Mobile Gaming / Performance Marketing

Backend Engineer

• Build and maintain scalable backend services and APIs using Node.js (NestJS) , writing clean, testable, maintainable code.
• Design and implement complex features with a long-term view (extensibility, stability, and operability).
• Improve architecture and maintainability across core systems (clear boundaries, clean interfaces, pragmatic patterns).
• Own performance and reliability outcomes: monitor production, identify bottlenecks, optimize latency, and strengthen resilience.
• Collaborate with DevOps to keep CI/CD, deployments, and infrastructure stable and efficient (cloud, containers, orchestration/IaC).
• Partner with Product, Design, Frontend, and QA to ship high-quality releases, translating requirements into clear technical specs and trade-offs.
• Raise the engineering bar via code reviews, mentoring, and standards that improve team output over time.

Senior Backend Engineer, Node.js, NestJS

Manager

• Run the full delivery lifecycle across backend, frontend, mobile, QA, and design.
• Facilitate sprint planning, backlog grooming, refinement, and daily coordination to maintain pace and alignment.
• Drive accurate estimation, strong task decomposition, and realistic commitments.
• Monitor progress, spot risks early, and remove blockers before they affect timelines.
• Keep releases predictable by tightly coordinating with engineering, QA, and product.
• Create, maintain, and continuously improve delivery workflows, internal processes, and documentation standards.
• Ensure task write-ups are consistently clear: requirements, acceptance criteria, and expected outcomes.
• Keep the backlog structured and easy to navigate in partnership with product and engineering leads.
• Set up clean communication paths and streamline handoffs between teams.
• Align product priorities with engineering capacity and design readiness.
• Make sure teams have the right context and inputs at each delivery stage.
• Support post-release reviews, retrospectives, and ongoing process improvements.

Product Delivery Manager

Data Scientist

• Define the data science vision and roadmap (what to build, why, and in what order).
• Hire, mentor, and set standards for a high-performing Data team.
• Own vendor delivery and coordination (external agency) until the internal team is established.
• Partner closely with product, engineering, marketing, operations, fraud, and leadership; turn business goals into measurable outcomes.
• Communicate complex model tradeoffs clearly to non-technical stakeholders.
• Lead the applied AI/LLM direction for product experiences and internal tooling.
• Build and maintain models for personalization/recommendations, engagement prediction, churn, and LTV/forecasting.
• Own the full model lifecycle: training, evaluation, deployment, monitoring, and retraining.
• Work with engineering to integrate models into backend services and client experiences with performance and scalability in mind.
• Design and ship LLM-enabled capabilities (e.g., semantic search, assistants, automation, content generation, decision support).
• Evaluate and adapt foundation models (fine-tuning/distillation/prompting) for product and internal workflows.
• Develop anomaly detection and behavioral models to identify suspicious activity.
• Create scoring and classification systems for patterns like manipulation, multi-accounting, and other abuse signals.
• Partner with product/ops to design thresholds, rules, validation systems, and automated risk workflows.
• Track emerging fraud trends and proactively address new attack vectors.
• Define requirements for scalable pipelines supporting training, inference, experimentation, and monitoring.
• Implement practical MLOps standards (CI/CD for models, versioning, monitoring, automated retraining).
• Raise the bar for data quality, documentation, and metadata practices.
• Build infrastructure patterns for LLM workloads (vector search, embeddings pipelines, evaluation, safety checks).

AI Engineer

Senior UA Manager – Incentivized Growth, Offerwalls, Rewarded Networks

Account Manager, Native Chinese Speaker – Mobile Gaming / Performance Marketing

Senior Backend Engineer, Node.js, NestJS

Product Delivery Manager

Head of Data