Jobs at adjoe

Develop and execute employer branding and talent attraction for a global adtech company: define EVP, produce video/podcast/tech content, run engineering blog, audit career sites and job copy, support developer conference speaking, and manage creative production and content pipelines to showcase employee journeys and technical stories.

Senior marketing leader to own global B2B SaaS/AdTech marketing strategy, GTM and product marketing, demand generation, brand and PR, events, and team leadership. Manage global budget, build systems and playbooks, align marketing to revenue, and lead regional teams across publishers and advertisers.

Lead technical strategy and integration for top enterprise partners, manage joint roadmaps, perform deep root-cause analysis, and use SQL/Python and analytics dashboards to drive data-driven decisions and account health for large mobile ad integrations.

Design and automate large-scale data pipelines (dbt, Airflow), optimize Trino/Athena SQL over multi‑TB daily volumes, implement memory-efficient Python processing, run rigorous A/B test analysis, mentor analysts, and translate analytics into stakeholder-facing dashboards and recommendations.

Lead development and execution of sales strategy to onboard and retain gaming and mobile app publishers for adjoe Ads. Build and manage pipeline using CRM, collaborate with product marketing, inform product strategy from publisher feedback, represent the company at industry events, and report to the Managing Director.

Analyze commercial ad data and KPIs, create and communicate reports, gather and document business requirements, model processes, identify improvements and risks, outline business impact, and implement and test solutions cross-functionally.

Lead and execute global creative direction for adjoe, managing a 4-5 person design team across time zones. Ensure brand consistency across channels, translate commercial objectives into high-converting design, develop scalable templates and brand guidelines, and deliver assets for web, events, presentations, data visualization, and performance marketing. Act as player-coach and collaborate closely with Growth, Product Marketing, and Content.

Build and maintain high-throughput Go microservices handling 2M+ daily interactions. Design event-driven pipelines (Kafka/SQS/SNS/Kinesis), optimize APIs and queries across DynamoDB, MySQL, Druid, and Athena, integrate ML algorithms with the Data Science team, implement secure callbacks (OAuth2, mTLS, HMAC), and collaborate across international teams. Use Terraform, Docker, and serverless tooling to deploy reliable, scalable systems.

Own demand and supply partnerships in Korea: source and close mobile gaming advertisers and app publishers, manage pipeline, conduct market research, onboard clients, prepare sales presentations, and coordinate with internal teams to grow revenue and optimize partner monetization.

Drive enterprise publisher acquisition and revenue in ANZ: manage full sales pipeline, generate leads, close deals, onboard and grow clients, prepare custom presentations, track activity in CRM, attend events, and communicate partner needs internally.

Identify and prospect potential app publisher partners, run high-volume outreach, support sales with collateral, maintain Salesforce pipeline, perform market and competitor research, coordinate and staff events, collaborate with marketing, and manage small projects within an international team.

Build and operate high-performance backend services and real-time analytics for advertising dashboards. Design microservices, ingest and process massive event streams (Kafka/Kinesis), optimize Go services for high throughput, and ensure low-latency queries against Druid and MySQL. Deliver well-documented APIs for partner-facing dashboards and collaborate with frontend teams using TypeScript/React. Contribute to end-to-end service ownership and infrastructure improvements.

Design, build, and productionize large-scale recommendation and bidding ML models. Own the full ML lifecycle from feature discovery and training to deployment, real-time low-latency inference, monitoring, and A/B experimentation. Collaborate with backend and data engineering teams to integrate models and iterate on performance.

Lead technical analyses to detect and prevent ad fraud at scale. Build and optimize SQL/Python data pipelines, apply anomaly detection methods, design experiments, automate reusable analytics frameworks, and communicate findings to technical and non-technical stakeholders to drive business decisions.

Create and maintain developer-facing documentation (API references, SDK guides, integration tutorials, sample apps). Build demo apps in Swift/Kotlin, contribute to Docusaurus/Markdown tooling, use JavaScript/TypeScript/HTML for components, debug integrations using SQL, logs, and dashboards, and collaborate cross-functionally to improve developer experience and reduce integration friction.

Lead the Dashboards team as a senior Go engineer and architect: design and operate adtech-scale distributed systems, build real-time analytics pipelines and dashboards (50M+ daily events), own partner-facing APIs and microservice integrations, optimize columnar DB queries (Druid/ClickHouse), work with TypeScript/React frontends, and manage and develop engineering team members while aligning technical decisions with business needs.

Support Business Operations by researching and analyzing companies, monitoring KPIs, creating advertiser campaigns, maintaining Salesforce data hygiene, assisting day-to-day operations, and evaluating AI tools to automate team workflows.

Own the Analytics Dashboard product end-to-end: define roadmap, manage a dedicated development team, run agile delivery, prioritize by user and business KPIs, bridge business and engineering, deliver 0-to-1 iterations, and grow team members while optimizing campaign management and real-time analytics.

Lead and grow the Data Science team owning the real-time bidding algorithm, combine hands-on modeling with people leadership, architect and deploy large-scale ML systems, run experiments and A/B tests, and align model work with product and infrastructure priorities to drive ad monetization and business outcomes.

Build and maintain ML-scale data infrastructure: feature store, real-time and batch pipelines, data quality and validation, and low-latency systems. Translate Data Science needs into high-performance Flink streams, eliminate ETL bottlenecks, collaborate on online ML systems, and implement monitoring, governance, and deployment processes for production ML workflows.