About Appier 

Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers businesses to create value through cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of “Making AI Easy by Making Software Intelligent,” Appier helps businesses turn AI into ROI through its Ad Cloud, Personalization Cloud, and Data Cloud—each powered by Agentic AI that enables autonomous, adaptive, and real-time decision-making. Today, Appier operates 17 offices across APAC, the US, and EMEA, and is listed on the Tokyo Stock Exchange. Learn more at www.appier.com.

About the Role

We are looking for a Senior Machine Learning Scientist to join the Enterprise Solution Science Team.
This team focuses on applying cutting-edge ML technologies to real-world marketing problems by combining them with omnichannel customer data.
In this role, you will be responsible for end-to-end development, from problem definition to driving model deployment into production.

What You’ll Work On

• Work on one or more of the following topics: user intention prediction, user segmentation, product recommendation, campaign content optimization, and send time optimization
• Collaborate with product teams to translate product goals and business needs into well-defined ML problems during the planning stage
• Work closely with ML engineers to deploy models to production, ensuring reliability and maintainability
• Conduct offline and online evaluation, monitoring, and continuous optimization of models after deployment
• Stay up to date with the latest research and proactively propose innovative applications

What We’re Looking For (Minimum)

• Master’s or PhD degree in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or related fields.
• 3+ years of experience applying ML techniques in areas such as prediction, optimization, segmentation, or recommendation.
• Proficient in Python and commonly used ML frameworks such as PyTorch or TensorFlow.
• Impact-driven mindset, strong analytical and problem-solving skills, and a continuous passion for learning cutting-edge technologies.
• Proficient in AI-powered development tools such as: Claude Code, OpenAI Codex / Codex CLI, Cursor, Windsurf, GitHub Copilot.

What We’re Looking For (Preferred)

• Practical experience in deploying enterprise-grade ML models, including model pipeline and performance dashboard development.
• Experience in cross-functional collaboration and leading industry projects.
• Industry experience in MarTech and passion for building customer-centric products.

#LI-AK1

Realize your potential by joining the leading performance-driven advertising company!

As a Algorithm Engineer on the R&D Team in our Taipei office, you’ll play a vital role in developing cutting-edge machine learning models that power personalized content recommendations for over 1 billion users worldwide. You’ll research, build, and deploy AI-driven solutions that enhance user engagement and drive business growth.

To thrive in this role, you'll need:

• Master’s degree in CS/CE, EE, Physics, Applied Mathematics, or a related field (Ph.D. is a plus).

• 1+ year of full-time work experience (or 3+ months internship for fresh graduates) in data science.

• 3+ years of programming experience in Python, Java, or Scala.

• Hands-on experience with machine learning frameworks like PyTorch or TensorFlow.

• Strong SQL and database skills for data extraction and analysis.

Bonus points if you have:

• Experience in content understanding or behavioral prediction models.

• Knowledge of optimization algorithms for business applications.

How you’ll make an impact:

As a Algorithm Engineer, you’ll bring value by:

• Researching and proposing data-driven solutions for personalization and targeting.

• Building and deploying machine learning models to enhance content recommendations.

• Developing optimization algorithms to meet key business objectives.

• Creating and maintaining data pipelines for ML model training and evaluation.

• Collaborating with product and engineering teams to productionize AI solutions.

• Presenting insights and model performance to stakeholders in a clear, actionable way.

Why Taboola?

If you ask Taboolars what they love about working here, they’ll tell you they’ve been empowered to realize their full potential while growing and learning from smart, talented peers. They’ll also share more about:

• Adam Singolda, Taboola Founder and CEO, says: “You can copy anything from another business, but you can’t copy a company’s culture.”

• Well-being: Enjoy comprehensive benefits, a fully stocked kitchen, and location-specific perks.

• Flexibility: Hybrid work model with 3 days in-office and the option to come in more often if desired.

• Work with the biggest names: We work with some of the biggest names in the business. Our publisher partners include Yahoo, Conde Nast, Fox Sports, NBCU, ESPN, CBS, and E! Online. Our advertiser clients include Wells Fargo, Honda, Pinterest, Expedia and Honda.

Ready to realize your potential?

Taboola is an equal opportunity employer. We value diversity and are committed to fostering an inclusive environment. Employment is based on qualifications, merit, and business need.

Learn more about #TaboolaLife on LinkedIn, Facebook, Instagram, X, YouTube, & the Taboola Life Blog.

About Taboola

Taboola empowers businesses to grow through performance advertising technology that goes beyond search and social and delivers measurable outcomes at scale.

Taboola works with thousands of businesses who advertise directly on Realize, Taboola’s powerful ad platform, reaching approximately 600M daily active users across some of the best publishers in the world. Publishers like NBC News, Yahoo, and OEMs such as Samsung, Xiaomi and others use Taboola’s technology to grow audience and revenue, enabling Realize to offer unique data, specialized algorithms, and unmatched scale.

#LI-Hybrid

#LI-MT1

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) 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.

Open to overseas candidates/Visa Support

This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing, and personalized content recommendation, while ensuring system robustness and scalability in a dynamic market environment.

What You’ll Work On

• Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes.
• Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization.
• Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions.
• Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning).

What We’re Looking For

• Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred.
• 4+ years of industry experience in ad tech, with a focus on performance optimization.
• Proven experience in applied machine learning, especially in CTR prediction, recommendation systems.
• Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills—able to lead end-to-end projects across product, data, and engineering.
• Bonus: Experience working on high-throughput, low-latency real-time systems (e.g., RTB engines, stream inference).

#LI-AK1

About Appier 

Appier (TSE: 4180) is an AI-native Agentic AI as a Service (AaaS) company that empowers businesses to create value through cutting-edge AdTech and MarTech solutions. Founded in 2012 with the vision of “Making AI Easy by Making Software Intelligent,” Appier helps businesses turn AI into ROI through its Ad Cloud, Personalization Cloud, and Data Cloud—each powered by Agentic AI that enables autonomous, adaptive, and real-time decision-making. Today, Appier operates 17 offices across APAC, the US, and EMEA, and is listed on the Tokyo Stock Exchange. Learn more at www.appier.com.

About the role

AI is reshaping how brands connect with consumers — and at Appier, we’re at the forefront. Our Playable Ads team builds AI-powered ad experiences — from interactive playable formats to video ads — that drive dramatically higher engagement and conversion for the world’s leading apps and games. We’re looking for a Senior Machine Learning Scientist to push the boundaries of what’s possible — applying cutting-edge ML, generative AI, and optimization techniques to create smarter, more compelling ad creatives at scale.

If you’re excited about turning the latest advances in AI into products that reach millions of users, and you thrive at the intersection of research and real-world impact, we’d love to talk.

What You’ll Work On

• Apply ML and generative AI techniques to problems such as creative optimization, user behavior prediction, engagement modeling, and dynamic content generation for various creative formats.
• Own the end-to-end ML lifecycle — from problem framing and experimentation through to production deployment and continuous improvement.
• Collaborate closely with product, engineering, and design teams to translate business goals into well-defined ML solutions.
• Conduct offline and online evaluation, A/B testing, and performance monitoring to continuously improve model quality and business impact.
• Stay current with the latest research in areas like LLMs, multimodal models, reinforcement learning, and recommendation systems, and proactively propose innovative applications to the team.
• Mentor fellow scientists and contribute to the team’s technical standards and culture.

What We’re Looking For

[Minimum qualifications]

• Master’s or Ph.D. in Computer Science, Machine Learning, Mathematics, Electrical Engineering, or a related field.
• Solid understanding of modern ML models and algorithms, with hands-on experience in at least one area such as prediction/optimization, generative models, recommendation systems, computer vision, or reinforcement learning.
• Proficient in Python and ML frameworks such as PyTorch or TensorFlow.
• Practical experience taking ML models from prototype to production, including pipeline development and performance monitoring.
• Strong analytical and problem-solving skills with the ability to diagnose model behavior and improve training pipelines.
• Clear communicator who can explain technical ideas to both technical and non-technical stakeholders and enjoys working in a fast-paced, collaborative environment.
• Proficient in using LLM-powered development tools (e.g., GitHub Copilot, Cursor, ChatGPT) to accelerate productivity.
• Ability to balance cutting-edge research with time-to-market constraints, delivering iterative PoCs that validate product hypotheses and translate into production-ready solutions.

[Preferred qualifications]

• Publications in top AI/ML conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, KDD, RecSys).
• Experience in the advertising technology or MarTech domain.
• Contributions to open-source projects or active involvement in research communities.

#LI-AK1

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) 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.

Open to overseas candidates/Visa Support

This position is based in Tokyo, Japan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth transition to Japan.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

Our engineering team has a solid technical background, which would be a great environment for a skilled engineer.

In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing, and personalized content recommendation, while ensuring system robustness and scalability in a dynamic market environment.

What You’ll Work On

• Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes.
• Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization.
• Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions.
• Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning).

What We’re Looking For

• Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred.
• 5+ years of industry experience in ad tech, with a focus on performance optimization.
• Proven experience in applied machine learning, especially in CTR prediction, recommendation systems.
• Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills—able to lead end-to-end projects across product, data, and engineering.
• Bonus: Experience working on high-throughput, low-latency real-time systems (e.g., RTB engines, stream inference).

Language

Fluent in English or Chinese.

#LI-AK1

About Appier 

Appier is an AI-native Agentic AI as a Service (AaaS) 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.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

In this role, you’ll directly impact the efficiency and profitability of ads campaigns by improving models for bidding, pricing, and personalized content recommendation, while ensuring system robustness and scalability in a dynamic market environment.

What You’ll Work On

• Design, implement, and productionize state-of-the-art ML models to improve campaign outcomes.
• Analyze large-scale user and auction data to discover predictive patterns and alpha signals that enhance bidding and personalization.
• Collaborate cross-functionally with engineering, product, and data teams to identify opportunities, define roadmaps, and deliver impactful solutions.
• Continuously improve system performance through offline experimentation and online testing (e.g., A/B tests, incremental learning).

What We’re Looking For

• Bachelor’s degree in Computer Science, Mathematics, EE, or related field; Master’s or PhD preferred.
• 4+ years of industry experience in ad tech, with a focus on performance optimization.
• Proven experience in applied machine learning, especially in CTR prediction, recommendation systems.
• Proficiency in Python and experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills—able to lead end-to-end projects across product, data, and engineering.
• Bonus: Experience working on high-throughput, low-latency real-time systems (e.g., RTB engines, stream inference).

#LI-AK1

About the role 

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. 

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). 

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. 

Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

• Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). 
• Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
• Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 

2. MLOps& Data Infrastructure: 

• Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. 
• Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
• Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

3. Cross-Functional Implementation & Leadership:

• Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. 
• Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
• Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 

4. Technical Strategy & Stakeholder Management:

• Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. 
• Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
• Vector Database: Experience with Vector DBs for RAG applications. 

Why Join Us? 

• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. 
• Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. 
• Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. 
• Compensation: Competitive salary and benefits package, commensurate with experience and performance. 

Application Process 

• If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
• If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn. 

Dcard 是在年輕族群有極高滲透率與影響力的社群平台。我們致力於打造一個讓每個人都可以放心分享自己故事的地方，讓平凡人分享不平凡故事的新世代社群服務。面對快速變化的社群生態，我們期待更多優秀的人才加入 Dcard。

為了達成這個目標，我們需要 Machine Learning Team Lead 加入我們，你將帶領 Dcard 的機器學習團隊，透過大規模使用者數據中發掘洞察，持續提升使用者體驗及產品黏著度。歡迎加入這個熱愛挑戰的技術團隊，一起打造被千萬人喜愛與使用的產品！

你將在團隊參與⋯

• 帶領 Dcard 機器學習團隊，制定並推動團隊目標。
  • 與產品及商業團隊密切合作，確保模型設計、資料流程與系統建構，高度對齊 Dcard 社群、廣告產品策略，並支持團隊成員的職涯發展與技能成長。
  • 負責推薦演算法的設計、開發與優化，並驅動推薦系統的技術創新，包含模型訓練、Feature pipeline 設計、個人化推薦策略，以及系統穩定性維護。
• 與 Infrastructure 及其他 ML/Engineering 等團隊緊密合作，共同建置 Dcard 機器學習相關系統。並從大量使用者數據中分析、挖掘洞察，以迭代演算法並優化產品體驗。
• 與團隊成員及公司技術高階主管共同制定技術發展願景與中長期 Roadmap，持續提升團隊技術力與影響力。

我們期待看到一個優秀的 Machine Learning Team Lead 具備以下能力

• 具備出色的技術領導能力，至少 1 年以上 Engineering Manager 的經歷或兩年以上工程專案管理經驗，曾經帶領不同技術背景、不同職能的夥伴達成共同目標。
• 五年以上軟體開發經驗，熟悉軟體專案開發與設計原則 (如 design principle, software architectures）。
• 熱愛思考使⽤者的需求，動手將演算法打造成產品。
• 精通 Python，並對學習新技術持開放態度，具備良好的軟體工程實踐經驗。
• 了解機器學習演算法及工作流程，例如 NLP、Deep Learning、Recommendation System 等。
• 重視程式品質與系統可維護性，面對需求能夠提出最小可行性系統架構及了解其中的 tradeoff。
• 具備良好的溝通與跨部門協作能力，能清楚表達技術決策、引導團隊對齊方向，並與其他團隊順利合作。

如果你擁有以下能力，那就更棒了！

• 有 Tech Lead 經驗或系統架構師經驗。
• 面對不熟悉的議題（非技術相關，如人事或組織議題），能夠拆解問題並思考討論解決方案。
• 具有高流量或者大型系統開發經驗。
• 能夠執行高品質的 code review or design review。
• 熟悉分散式系統設計，能夠處理巨量資料或開發大型系統。
• 熟悉機器學習系統商業應用場景、系統設計。
• 能夠解決主流 ML framework 在巨量資料開發時遇到的問題。
• 熟悉以下其中幾項技術：
  
  • PyTorch / Scikit-Learn / XGBoost / Tensorflow
  • Airflow
  • GCP / Kubernetes
  • SQL / NoSQL / Redis
  • Linux

薪資待遇

• 面議

注意事項

• 若不符合職位需求的面試者，將不另行通知。
• 如果尋找到合適的人選加入，職缺可能會提前關閉。
• 如果人選在申請過程中提供不實資訊，Dcard 有權取消雇用提議。
• 在 Dcard，每位夥伴都是平等的，我們重視每個人的多元性。Dcard 致力於打造一個多元與共融的環境，尊重並且包容每一位夥伴。我們相信，平等與多元能夠帶來無限創新的可能。因此，我們的招聘流程提供每位應徵者平等的就業機會，並落實無歧視的就業環境。

狄卡科技股份有限公司及其關係企業基於「員工招募」之目的，將蒐集您的「識別類、特徵類、社會情況、教育、考選、技術或其他專業、受僱情形」等個人資料，或您主動為求職目的而提供的資料（履歷、自傳等），以在招募期間及地區內，作為本次招募作業審核、評選、聯繫之用。如您未獲錄取，我們仍將保留您的資料以便向您通知新的職缺機會。

您得以聯繫狄卡科技股份有限公司及其關係企業（電子信箱：hr.ta@dcard.cc）行使請求查閱、補充、更正；請求提供複製本；請求停止蒐集、處理、利用；請求刪除個人資料等權利。如您未正確、完整提供各項資料，將可能影響聘用審核、評選或聯繫。

About the role 

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. 

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). 

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. 

Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

• Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). 
• Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
• Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 

2. MLOps& Data Infrastructure: 

• Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. 
• Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
• Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

3. Cross-Functional Implementation & Leadership:

• Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. 
• Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
• Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 

4. Technical Strategy & Stakeholder Management:

• Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. 
• Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers. 

• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers. 

• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.). 

• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control. 

• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM. 

• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA). 

• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection). 

• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications. 

• Vector Database: Experience with Vector DBs for RAG applications. 

Other Benefits 

To us, people are our greatest asset, and we are more than happy to invest in employees! We create a healthy work atmosphere and provide you with the tools and support for doing your job successfully. With a culture of flexibility and transparency, we believe there should be no barriers, and everyone’s contributions matter. 

Work Life Balance is a must  

• 15 days annual leaves (pro-rata for partial month at first year) 
• 5 days full-pay sick leaves, 3 days menstrual leaves 
• Health check subsidy 
• Ergonomic-design chair and fully-equipped devices for work
• Hybrid remote work and flexible working hour.

Grow together & keep learning

• Conferences & external subsidy 
• Learning clubs to share technical skill (e.g: Frontend/Backend tech sharing, Blockchain...etc) 

Work Hard, Play even Harder 

• Various entertainment & sports clubs, attend basketball clubs today, and play board game tomorrow! 
• Snacks & beverage to refill your energy anytime 

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.

The Impact You’ll Make at Appier

Appier is seeking a Senior Machine Learning Scientist to join our Advertising Cloud Optimization team, which leads the development of core machine learning algorithms driving campaign efficiency and advertiser ROI. Our programmatic advertising platform operates at a massive scale, handling over multi millions queries per second (QPS), all powered by our proprietary deep learning models for bidding, pricing, and personalized content delivery.

In this role, you'll measure ads incrementality on different type of traffic, creative, users, and also improve campaign efficiency on combination having good incrementality by machine learning model and AI automation.

What You’ll Work On

• Leverage scientific methods to improve ads measurement.
• Drive online experiments to continuously improve ads effectiveness.
• Own the project independently.
• Partner with product stakeholder, backend and frontend to provide measurement solution for our customers.

What We’re Looking For

• Master’s or PhD’s degree in Computer Science, Machine Learning, Statistics, Econometrics, or related field.
• 3+ years of industry experience on causal inference, incrementality, marketing mix modeling or other measurement solutions on digital Ads related fields, and understand the assumption and difference across methods.
• Proficiency in Python and SQL experience with modern ML frameworks (PyTorch, TensorFlow, etc.).
• Strong ownership and collaboration skills.
  

#LI-AK1

About the role 

We are looking for a talented Machine Learning Engineer to join our Product Core Engineering team. You will be responsible for building and optimizing machine learning workflows that directly power our AI-driven products. This role focuses on the full lifecycle of model development — from training and fine-tuning to deployment and monitoring — ensuring robust and efficient ML systems at scale.

Why Join Us?

• Product Impact: Your work will be directly embedded in our core AI products, shaping user experience and product capabilities.
• Engineering Excellence: Be part of a team that values high-quality engineering, reproducibility, and scalability.
• Innovation: Opportunity to experiment with cutting-edge ML and GenAI technologies in production settings.
• Collaboration: Work alongside backend, platform, and product teams in a highly collaborative environment.
• Competitive Package: Receive attractive compensation and benefits aligned with your skills and performance.

Key Responsibilities

• Model Development: Design and implement training processes for machine learning classifiers and generative models.
• Fine-tuning & Prompting: Adapt pre-trained models to specific product needs through fine-tuning, prompt engineering, and parameter optimization.
• Hyperparameter Management: Configure and tune hyperparameters to balance accuracy, robustness, and performance.
• Pipeline Engineering: Build scalable training and evaluation pipelines to support continuous experimentation.
• Integration: Collaborate with backend and product engineers to deploy models into production systems.
• Monitoring & Maintenance: Establish monitoring metrics and retraining strategies to maintain model performance in dynamic environments.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
• Proven experience in training classifiers and fine-tuning transformer-based models.
• Strong understanding of tokenization techniques, embedding models, and vector representations.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.
• Familiarity with hyperparameter tuning and model configuration best practices.
• Experience working with large-scale datasets and building reproducible ML pipelines.
• Fluent in Mandarin; proficiency in English is an advantage.

Desired Skills

• Knowledge of cloud platforms (AWS, Azure, GCP) and containerized deployment (Docker, Kubernetes).
• Experience with LLM prompting and fine-tuning is a plus.
• Basic understanding of data engineering practices (ETL, data validation, feature engineering).
• Exposure to AI safety, security, or bias mitigation techniques is an advantage.

Other Benefits 

To us, people are our greatest asset, and we are more than happy to invest in employees! We create a healthy work atmosphere and provide you with the tools and support for doing your job successfully. With a culture of flexibility and transparency, we believe there should be no barriers, and everyone’s contributions matter. 

Work Life Balance is a must  

• 15 days annual leaves (pro-rata for partial month at first year) 
• 5 days full-pay sick leaves, 3 days menstrual leaves 
• Health check subsidy 
• Ergonomic-design chair and fully-equipped devices for work
• Hybrid remote work and flexible working hour.

Grow together & keep learning

• Conferences & external subsidy 
• Learning clubs to share technical skill (e.g: Frontend/Backend tech sharing, Blockchain...etc) 

Work Hard, Play even Harder 

• Various entertainment & sports clubs, attend basketball clubs today, and play board game tomorrow! 
• Snacks & beverage to refill your energy anytime