AI Engineer - Playable Ads
About the job
- A bachelor's or master's degree in Computer Science, Machine Learning, Electrical Engineering, or a related field, or equivalent practical experience.
- At least 3 years of experience building production software or machine-learning systems, with strong computer science fundamentals and a record of writing maintainable, tested code.
- Strong Python skills and hands-on experience with PyTorch, TensorFlow, or equivalent machine-learning tooling.
- Practical experience taking models, prompts, or data workflows from prototype to production using APIs, containers, batch or stream processing, and cloud infrastructure.
- Experience with cloud and machine-learning platform tooling such as Docker, Kubernetes, Google Cloud Platform, continuous integration and continuous delivery, workflow orchestrators, message queues, Spark, or OpenTelemetry.
- Understanding of production machine-learning concerns, including reproducibility, data and model versioning, evaluation, monitoring, failure handling, latency, and cost.
- Strong debugging and systems-thinking skills across distributed components such as queues, workers, storage, and external services.
- Clear communication and cross-functional collaboration skills, and comfort using AI-assisted development tools responsibly while validating their output.
- Build and operate reliable, scalable machine-learning and generative AI pipelines that power automated content creation, personalization, and optimization across creative formats and products.
- Productionize research prototypes and models by designing service and API contracts, containerized workers, asynchronous orchestration, artifact storage, and clear ownership boundaries.
- Apply modern machine learning, including large language models, vision-language models, multimodal generation, and agentic tool use, to improve creative quality, automation, and personalization.
- Engineer reliable workflows with schema validation, idempotency, retries, failure recovery, security, versioning, and end-to-end tests across staging and production.
- Define and monitor quality, latency, failure-rate, and generation-cost metrics; use logs, traces, evaluations, and experiments to diagnose bottlenecks and improve outcomes.
- Collaborate with scientists, backend and frontend engineers, product, and design to translate business needs into maintainable machine-learning systems, document handoffs, and ship iteratively.
- Experience with generative media, creative optimization, advertising or MarTech, recommendation, or content-generation products.
- Hands-on experience with generative AI or multimodal systems, such as large language models, vision-language models, image or video generation, retrieval-augmented generation, tool use, or agent frameworks.
- Experience designing agent tools, feedback-loop pipelines, rigorous evaluations, or online experiments, and translating research into measurable product impact.
About the company
Appier provides AI-native SaaS tools for advertising and marketing, helping businesses manage digital campaigns and customer experiences. Its products include Ad Cloud for automated targeting and optimization, Personalization Cloud for real-time cross-channel personalization, and Data Cloud (AIXON) to unify data sources for audience insights. The platform stands out by integrating these capabilities through a series of acquisitions (e.g., QGraph, Emotion Intelligence, BotBonnie, Woopra, AdCreative.ai) to cover the full marketing funnel with AI-driven automation. Its goal is to help brands acquire customers, engage them effectively, and increase conversions using data-driven decisions.
Company Size
501-1,000
Company Stage
IPO
Headquarters
Taipei, Taiwan
Founded
2012
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Simplify's Take
What believers are saying
- Q2 2026 revenue guidance of JPY12.5-12.7 billion follows record Q1 and Q2.
- Gross margin reached 53.9% in Q1 2026, showing stronger operating leverage.
- Omio expanded with Appier from Spain to 21 markets while meeting CPA targets.
What critics are saying
- Google and Meta keep compressing adtech margins as self-serve automation improves.
- Appier's Q2 2026 profit guidance depends on continued enterprise spending and execution.
- If agentic AI features commoditize, Appier becomes a replaceable marketing workflow vendor.
What makes Appier unique
- Appier pairs proprietary Ad Cloud, Personalization Cloud, and Data Cloud with agentic AI.
- Its 2026 research translates into products faster than most adtech peers.
- Tokyo-listed, Taiwan-founded, with 17 offices across APAC, the US, and EMEA.
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0%Appier research accepted at NeurIPS: AI agents learn not only to use tools, but to build their own. Sep 30, 2026, 09:06 ET New SMITH framework lets small models rival larger ones with far fewer tokens, paving the way for scalable multi-agent collaboration SINGAPORE, Sept. 30, 2026 /PRNewswire/ - Appier (TSE: 4180), an AI-native company delivering Agentic AI as a Service (AaaS), today announced its latest research paper, "Joint Optimization of Tool Creation and Use for Large Language Model Agents," accepted at NeurIPS, the world's premier AI and machine learning conference, often called "the Olympics of AI." The paper introduces SMITH (Schema-grounded Multi-task Iterative Tool Honing), a reinforcement learning framework that lets AI build tools and use them effectively in a single training loop, with each tool continuously refined based on real-world problem-solving results. The breakthrough tackles a key challenge in Agentic AI: models can create tools, but often struggle to use them well. The research shows that small models trained with SMITH can build reusable tools that rival those created by much larger models, even on tasks they have never seen. These tools can also be shared across models and tasks, significantly reducing the token cost of repeated reasoning. The paper's acceptance at NeurIPS highlights Appier's research strength in improving AI agent efficiency and advancing multi-agent collaboration, and reinforces its position at the forefront of global Agentic AI research. "Humans turn their problem-solving experience into tools, so they never have to start from scratch. AI agents are now evolving in the same way," said Dr. Chih-Han Yu, CEO and Co-founder of Appier. "This research shows that agents can learn to build tools, continuously refine them, and share proven tools across models of all sizes, making multi-agent collaboration more efficient and scalable. NeurIPS's acceptance of this paper further recognizes Appier's forward-looking research and innovation. We will continue to bring Agentic AI into real-world applications and deliver measurable results for businesses." AI agents must build tools and know whether they work As Agentic AI handles more enterprise workflows on its own, selecting and calling the right external tools has become critical for real-world deployment. Yet many AI systems still rely on engineers to build APIs or configure fixed tools in advance, which must be rebuilt whenever data sources, tasks, or business needs change. Even when AI can create its own tools, existing methods typically assign creation and use to separate models. As a result, the tool-building model gets little feedback on real-world performance, and cannot easily tell whether a tool is clearly described, works reliably, or can be called correctly by other models. SMITH puts both skills into one training loop, so the AI learns to build good tools and use them well at the same time. When a tool has a vague description, poorly designed parameters, or fails to run, that result feeds back into the model. A closed loop of building, using, verifying, and refining keeps improving tool quality. The study found that training tool creation and tool use together clearly beats training them separately. "When SMITH trains a model to use tools, it sees only the tool's description and parameter specifications, not the underlying code," said Chieh-Yen Lin, Research Scientist at Appier. "This makes the clarity of each description, and whether the tool can be called correctly, direct feedback during training. Our experiments also confirmed that other models can use these tools to solve problems more effectively. Looking ahead, we hope to build models that can continuously interact with their environment and take on a wider range of tasks." Learning from 4 examples, proven on 16 new problems SMITH trains from easy to hard. The AI first learns a method from 4 simple examples and builds tools from it. It then tests the model on 16 harder, previously unseen problems to see whether it can generalize. It keeps only tools that solve new problems and adds them to a shared tool library that multiple AI agents can use. As more high-quality tools come in, better ones replace weaker ones. Instead of building new tools for every task, agents can build and reuse proven problem-solving skills. The research delivers three key findings: * Small models can outperform larger ones at tool creation: A model of about 4 billion parameters trained with SMITH built tools that outperformed those from every other method in the study on unseen tasks. It even beat a baseline in which a roughly 30-billion-parameter model built tools on the fly. Effective tool creation doesn't have to depend on bigger models. * Proven tools work across model sizes: Tools built by the small model handled new tasks well even when used by a lightweight model of only about 350 million parameters. The same tools also boosted the performance of larger models, allowing agents to divide work more flexibly and efficiently. * Repeated reasoning becomes a reusable tool: SMITH turns repeated reasoning into tools that can be called directly. In experiments, average output fell from 3,206 tokens with conventional step-by-step reasoning to about 100 tokens, roughly a 32-fold gain in efficiency. When facing similar problems, the AI doesn't need to run the full reasoning process every time, maintaining task performance while speeding up reasoning and lowering compute costs. This research opens a new direction for enterprise Agentic AI. Many daily operations are repetitive, from converting financial metrics and processing data to querying reports, checking rules, and routing customer service cases. AI could turn these scattered methods into proven, shared tools that any agent can call, cutting the cost of repeated development and reasoning. In advertising and marketing, agents handling customer data, personalization, customer service, and ad buying can share proven tools and consistent business rules to collaborate more efficiently. Whether entering a new market, onboarding a new advertiser, or starting with limited data, businesses can turn past successes into verifiable, scalable AI capabilities that help agents adapt to new tasks faster. Appier will continue to advance Agentic AI through forward-looking research, making AI a core engine of long-term business growth. About Appier Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers business decision-making with cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of "Making AI Easy by making software intelligent," Appier endeavors to help businesses turn AI into ROI with its Ad Cloud, Personalization Cloud, and Data Cloud solutions. Now Appier has 17 offices across APAC, the US and EMEA, and is listed on the Tokyo Stock Exchange. Visit www.appier.com for more company information, and visit ir.appier.com/en/ for more IR information. SOURCE Appier
Cherrypicks drives AI Playbook to enhance marketers' performance. How can marketers use AI - powerfully and responsibly? The IAB HK AI Marketing Playbook event brought together AI and Tech Committee members, including Meta, WPP, Havas, Appier, and Omnicom - delivering practical, cutting-edge insights from GEO & AI Search to Agentic AI Media Buying across Eastern and Western tech ecosystems. Its Vice President, Terence Wong, shared Cherrypicks' perspective on Responsible AI - the principle that guides every AI solution Cherrypicks build. Terence shared key insights on how marketers can embrace AI responsibly. AI can move fast, but without transparency and ethical guardrails, speed becomes a risk. It starts with building governance in from day one - not bolting it on as an afterthought. And above all, keeping humans not only in the loop, but driving the loop where it matters most. Because the most effective AI marketing isn't fully autonomous - it's Human + AI, not Human vs AI. Cherrypicks is committed to bringing its experience in building enterprise AI solutions to help the marketing community work smarter, faster, and more responsibly. Thank you to Meta for hosting and IAB Hong Kong for a full-house afternoon.
Appier lifts FY26 outlook after record second-quarter revenue and margin gains. Chong Jing, Taipei Aug 17, 2026, 07:50 0 Credit: DIGITIMES Appier, an agentic AI-as-a-service provider, reported record second-quarter 2026 revenue of JPY12.9 billion and raised its fiscal 2026 outlook on the back of stronger growth and higher profitability... Picks for you
Appier recognized by Gartner as a representative vendor in AI-Native applications and solutions category. 2026/05/19 Taipei, Taiwan - May 19, 2026 - As an AI-native Agentic AI-as-a-Service (AaaS) company, Appier today announced it has been recognized as a representative vendor in the 2026 Gartner(R) Product Leader Insight: Leverage AI-Native Startups for Cost-Effective Innovation research, in the AI-native applications and solutions category, reinforcing its position as a leading AI-native company across the modern AI tech stack. As the only Japan-listed company mentioned in the report, Appier believes this recognition highlights its strength in building production-ready, outcome-driven AI platforms that deliver measurable business impact for enterprises. Appier is also acknowledged for leveraging its data moat to enable personalized marketing agents, further reinforcing its technical leadership and differentiation in the rapidly evolving Agentic AI landscape. ### About Appier Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers business decision-making with cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of "Making AI Easy by making software intelligent," Appier endeavors to help businesses turn AI into ROI with its Ad Cloud, Personalization Cloud, and Data Cloud solutions. Now Appier has 17 offices across APAC, the US and EMEA, and is listed on the Tokyo Stock Exchange. Visit www.appier.com for more company information, and visit ir.appier.com/en/ for more IR information. Gartner Disclaimer Gartner, Product Leader Insight: Leverage AI-Native Startups for Cost-Effective Innovation, Tracy Tsai, Kelli Smith, Mark Wesker, 1 May 2026. GARTNER is a trademark of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner's business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. You may also like.
Appier beats Q1 revenue and profit targets with Agentic AI-driven growth. May 13, 2026, 04:47 ET Outperformed profitable growth underscores an optimistic outlook from Q2 onward Highlights and achievements of Q1 FY26 * Gross profit surged to JPY 6.5 billion, with a growth rate of 35.9%, while gross margin jumped to 53.9% from 51.4% in the same period last year (54.7% on a constant currency basis). * Achieved record Q1 revenue of JPY 12.1 billion, up 29.4% YoY and at the high end of guidance. * Growth momentum accelerated globally, with US and EMEA revenue surging 49% YoY, while NEA achieved robust 28% YoY growth on a high base, and SEA accelerated to 4x YoY growth. This expansion was reflected vertically, with E-commerce up over 35% and Other Internet Services, including Online Travel, growing over 40% YoY. * Operating profit rose 153% YoY to JPY 185 million, with margin improving to 1.5% (Operating profit surged 473% YoY to JPY 418 million with a 3.5% margin on a constant currency basis) * Q1 outperformance laid a strong foundation for Q2, with revenue projected to reach JPY 12.5-12.7 billion, ahead of the initial forecast and tracking above plan. Agentic AI leadership drives Q1 outperformance with structural margin expansion TOKYO, May 13, 2026 /PRNewswire/ - Appier Group Inc. (TSE: 4180, "Appier"), an Agentic AI-native company, today announced its financial results for Q1 2026. Appier delivered a record Q1 gross profit of JPY 6.5 billion, up 35.9% YoY, with gross margin expanding structurally to 53.9% (54.7% on a constant currency basis), up from 51.4% in Q1 FY25. Revenue reached a record Q1 of JPY 12.1 billion, increasing 29.4% YoY and landing at the high end of guidance. This growth is driven by a go to market strategy focused on key verticals, reaching an upward profitability trend bolstered by scalable Agentic AI deployment, which drives operating leverage with in-house adaptation of Agentic AI as well. Both Q1 revenue and profit exceeded its initial plan, establishing a stronger baseline for further profitability from Q2 onwards. Growth momentum was fueled by the accelerated expansion across key regions and verticals. U.S. and EMEA (20% of revenue) surged 49% YoY, while Northeast Asia (70% of revenue) sustained robust 28% YoY growth on a high base. Southeast Asia also delivered an exceptional 4x YoY growth. The company's strategic focus on key verticals and high-value customers drove strong performance: over 35% YoY growth in E-Commerce and over 40% in Other Internet Services, led by the Online Travel sector (including recent case study customers such as Trip.com and Omio). Accelerating momentum within our high-value customer base, across diversified verticals and geographies, is driving revenue growth rates well above the company average. Globally recognized AI-native company accelerates operating leverage with Agentic AI Operating profit rose 153% YoY to JPY 185 million, expanding the operating margin to 1.5%. On a constant currency basis, operating profit surged 473% YoY to JPY 418 million with a 3.5% margin. This strong performance underscores continued robust profitable growth and AI-powered operational efficiency gains. As Appier transitions into an autonomous organization, its Agentic AI-driven automation efforts have yielded significant productivity gains across departments, resulting in a 27% YoY increase in gross profit per headcount. This accelerating structural profitability is reflected in Appier's ability to scale revenue and global operations with a stable headcount, highlighting the efficiency of its AI-native operating model. Customer revenue churn rate reached a seven-quarter low of 0.275%, reflecting strong Agentic AI product stickiness and high customer retention. Recurring revenue further increased to over 95% of total revenue, underpinning customer loyalty and scalable long-term growth. ARPC demonstrated robust momentum, growing 15% YoY on an FX-neutral basis, marking a significant acceleration compared to the same period last year of 10% YoY. Appier has been recognized as a representative vendor in the 2026 Gartner(R) Product Leader Insight research report in the AI-native applications and solutions category, reinforcing its position as a leading AI-native company across the modern AI tech stack. Being the only Japan-listed company mentioned in the report, the recognition defines Appier's strength in building production-ready, outcome-driven AI platforms that deliver measurable business impact for enterprises. Appier is also acknowledged for leveraging its data moat to enable personalized marketing agents, further reinforcing its technical leadership and differentiation in the rapidly evolving Agentic AI landscape. Accelerated Q2 momentum with performance tracking ahead of forecast Appier is signaling a bolstered outlook for Q2, reflecting increased confidence in its growth trajectory, with revenue projected to reach JPY 12.5-12.7 billion, surpassing the initial forecast. The company also expects a significant step-up in profitability, with Q2 operating income projected at JPY 1.0-1.2 billion, ahead of the initial forecast. This accelerated profit growth is driven by the impact of expanding operating leverage reinforced by the scalable deployment of Agentic AI solutions. "Appier is pleased with the recent recognition by Gartner as a representative AI-native company. Our Agentic AI capabilities, demonstrated through strong product offering and operational leverage, are the foundation for our outperforming Q1 results and optimistic future outlook," said Dr. Chih-Han Yu, CEO and Co-founder of Appier. "By placing Agentic AI at the core of Appier's operating model, we help enterprises automate workflows to achieve more precise, predictable, and hyper-personalized marketing outcomes. As a pioneer in Agentic AI, we continue to drive innovation by bridging cutting-edge research and real-world product deployment to help our customers achieve the best possible ROI." Outcome-driven Agentic AI built for enterprise growth Appier transforms ad campaigns from manual execution into continuously optimizing growth engines. Its Agentic AI-powered Ad Cloud combines buyer intent prediction, self-optimizing creative intelligence, and always-on incrementality optimization to help brands acquire high-value users, maximize ROAS through continuous campaign iteration, and validate every dollar in real time for measurable growth. Across its Agentic Enterprise platforms, Appier embeds industry-specific intelligence into scalable AI agents, enabling enterprises to unlock the value of trusted data through a flexible enterprise data hub with strong governance. Its orchestrated multi-agent architecture seamlessly enables trustworthy, self-aware agents to collaborate precisely across end-to-end enterprise workflows. Powered by Appier's proprietary AI foundation and deep cross-industry expertise, these platforms help enterprises securely scale AI adoption with a predictable outcome-driven impact. About Appier Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers business decision-making with cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of "Making AI Easy by making software intelligent," Appier endeavors to help businesses turn AI into ROI with its Ad Cloud, Personalization Cloud, and Data Cloud solutions. Now Appier has 17 offices across APAC, the US and EMEA, and is listed on the Tokyo Stock Exchange. Visit www.appier.com for more company information, and visit ir.appier.com/en/ for more IR information. Gartner Disclaimer Gartner, Product Leader Insight: Leverage AI-Native Startups for Cost-Effective Innovation, Tracy Tsai, Kelli Smith, Mark Wesker, 1 May 2026. GARTNER is a trademark of Gartner, Inc. and/or its affiliates. Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner's business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose. For media queries, please email [email protected] SOURCE Appier