Software Engineer, Ads Solutions
at Appier
Taipei, Taiwan, onsite, full time, 3+ years
posted 24 Aug 2026
- Kibana
- BigQuery
- VAST
- ClickHouse
- AppsFlyer
- CAPI
- SKAdNetwork
- Copilot
- Datadog
- Cursor
- Grafana
- SQL
- MMP
- Adjust
- S2S
- Branch
- Snowflake
- OpenRTB
- SDK
- Spark
- Windsurf
- Python
About the role
Posted: 2026-08-24
About Appier
Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.
Appier is seeking a Software Engineer, Ad Serving to be the deepest technical problem-solver behind our advertising products. When a campaign underdelivers, when CVR drops without an obvious cause, or when a partner integration behaves unexpectedly, you are the person who reproduces the issue, reads the logs, queries the data, and gets to the actual root cause — then explains it in language a client or an account team can act on.
You will work at the intersection of ad serving, data, and machine learning: diagnosing delivery and performance issues across the full funnel from bid request to conversion, validating partner and supply integrations, and turning what you find into concrete improvements to our bidding and supply strategy. You will also build the tooling and playbooks that let the rest of the organization resolve these issues without you.
[The seniority/title is determined by job-related skills, experience, and evaluation after the interview.]
What you would do
- Own Tier 2/3 escalations across ad serving, tracking, data pipelines, and ML-driven bidding
- Perform root cause analysis on delivery failures, pacing anomalies, and performance metric fluctuations (CTR/CVR/CPA/ROAS)
- Debug OpenRTB integrations, VAST/video, SDKs, MMP postbacks, and conversion tracking (S2S, CAPI, SKAdNetwork)
- Use SQL to analyze large-scale bid/impression/conversion data and isolate anomalies
- Analyze supply-side data (SupplyChain object, ads.txt, sellers.json) to strengthen delivery strategy and flag invalid traffic
- Build internal tools and playbooks to reduce investigation time and enable team self-service
- Validate new partner integrations pre-launch
- Partner with Product, Engineering, and Data Science to drive platform improvements
Read from job-boards.greenhouse.io, last seen 4d ago.