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.

About the role

Appier's Playable Ads team (https://www.appier.com/en/blog/what-is-playable-ads)  builds AI-powered playable and video ad experiences that drive dramatically higher engagement and conversion for the world's leading apps and games. We're looking for a Senior Backend Developer with deep expertise in distributed systems and modern backend architecture to design and build the scalable, high-performance services behind these experiences — from creative generation pipelines to the infrastructure that brings our ML models to production. You'll work across microservices, messaging systems, data persistence layers, and system performance optimization, while partnering with ML scientists on MLOps workflows to continuously ship and serve new model versions to end users. You'll also collaborate closely with frontend engineers and product managers to deliver seamless, cross-functional outcomes.

If you're passionate about building backend systems that operate at scale, thrive in cross-functional teams, and want your code to directly power AI-driven ad experiences reaching millions of users, we'd love to talk.

What You'll Work On

• Architect and develop robust, scalable backend services and APIs using ASP.NET Core, owning sizable projects from system design through production operation.
• Design efficient database schemas and optimize data access logic across SQL and NoSQL data stores.
• Implement message-driven communication with high reliability and fault tolerance.
• Partner with ML scientists to build and maintain MLOps pipelines — including model versioning, deployment, serving, and monitoring — ensuring new models are reliably delivered to production.
• Lead system designs, drive technical roadmap decisions, and provide guidance to team members.
• Proactively identify performance bottlenecks, profile critical components, and implement scalable solutions.
• Own and continuously improve system observability, incident management, CI/CD pipelines, and deployment workflows.
• Explore and integrate AI-assisted development tools and emerging technologies to boost team productivity.
• Participate in on-call rotation to ensure product reliability and scalability.

What We're Looking For 

[Minimum qualifications]

• Proficiency in C# and ASP.NET Core, or experience with other object-oriented languages (e.g., Java, Go) with willingness to cross-train in the .NET stack.
• Proven experience in distributed systems, microservice architectures, and designing scalable backend systems.
• Strong expertise in PostgreSQL, MongoDB, or equivalent SQL/NoSQL databases, with hands-on experience in ORM frameworks such as Entity Framework Core.
• Advanced knowledge of message broker systems such as RabbitMQ, Kafka, or NATS.
• In-depth understanding of IO-bound operations, threading, parallelism, and asynchronous programming.
• Practical experience with Docker, container-based deployment workflows, and cloud platforms (AWS, GCP, or Azure).
• Hands-on experience with CI/CD pipelines, observability tooling, and production operational practices.
• Demonstrated ability to write clean, well-documented code and solve complex problems analytically.
• Clear communicator who can explain technical decisions 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.

[Preferred qualifications]

• BS/MS degree in Computer Science or a related field.
• Experience with MLOps practices such as model serving infrastructure, A/B testing frameworks, or ML pipeline orchestration tools.
• Leadership experience such as mentoring junior engineers, leading code reviews, or facilitating agile processes.
• Experience with performance profiling and debugging tools, high-performance network services, or FFI integrations for performance-critical components.
• Solution-oriented mindset with a focus on system sustainability, and willingness to explore emerging technologies into backend systems.

[Language]

Fluent in English, Japanese language proficiency is not required.

Open to overseas candidates/Visa Support
This position can be based in Tokyo, Japan or Taipei, Taiwan. For international candidates, Appier's Japan office provides visa sponsorship to ensure a smooth relocation transition to Japan.

#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. 

Role Overview

We are seeking highly motivated and curious individuals to join our Machine Learning team at Kronos Research. In this role, you will bridge the gap between advanced deep learning and financial markets, designing robust models for medium and high-frequency systematic trading strategies. You will manage the full ML lifecycle, from researching novel architectures to deploying scalable, low-latency models that directly drive trading revenue.

Key Responsibilities

• Feature Engineering: Analyze complex time-series data, orderbook dynamics and trade data to engineer high-signal features
• Deep Learning Architecture: Design and train deep-learning based models (MLP, LSTM, RNN, Transformers, RL agents, etc) tailored for financial trading environments
• Backtesting & Evaluation: Conduct comprehensive backtesting and simulation across various asset classes and exchanges; analyze trade execution and PnL attribution
• Model Deployment: Collaborate with engineering teams to optimize and deploy models into production
• MLOps & Automation: Build and maintain automated pipelines for data ingestion, model retraining, and continuous performance monitoring to streamline the research-to-production workflow

Qualifications

• Strong academic or professional foundation in machine learning, quantitative research, and/or other related STEM fields; open to both experienced candidates and highly-motivated fresh graduates
• Deep understanding of neural network architectures and their application to time-series forecasting
• Proficiency in Python and modern ML frameworks (PyTorch/TensorFlow/Jax); C++ preferred
• Solid command of probability theory, linear algebra and applied statistics
• Strong communication skills and able to articulate technical concepts with clarity
• High level of drive, curiosity and a passion for continuously learning in a fast-paced environment

Role Overview

We are seeking highly motivated and curious individuals to join our Machine Learning team at Kronos Research. In this role, you will bridge the gap between advanced deep learning and financial markets, designing robust models for medium and high-frequency systematic trading strategies. You will manage the full ML lifecycle, from researching novel architectures to deploying scalable, low-latency models that directly drive trading revenue.

Key Responsibilities

• Feature Engineering: Analyze complex time-series data, orderbook dynamics and trade data to engineer high-signal features
• Deep Learning Architecture: Design and train deep-learning based models (MLP, LSTM, RNN, Transformers, RL agents, etc) tailored for financial trading environments
• Backtesting & Evaluation: Conduct comprehensive backtesting and simulation across various asset classes and exchanges; analyze trade execution and PnL attribution
• Model Deployment: Collaborate with engineering teams to optimize and deploy models into production
• MLOps & Automation: Build and maintain automated pipelines for data ingestion, model retraining, and continuous performance monitoring to streamline the research-to-production workflow

Qualifications

• Strong academic or professional foundation in machine learning, quantitative research, and/or other related STEM fields; open to both experienced candidates and highly-motivated fresh graduates
• Deep understanding of neural network architectures and their application to time-series forecasting
• Proficiency in Python and modern ML frameworks (PyTorch/TensorFlow/Jax); C++ preferred
• Solid command of probability theory, linear algebra and applied statistics
• Strong communication skills and able to articulate technical concepts with clarity
• High level of drive, curiosity and a passion for continuously learning in a fast-paced environment

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.

About the Role

We are looking for a Senior Software Engineer, Machine Learning 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 help bridge the gap between research and production by building and optimizing scalable, high-performance ML infrastructure — including data pipelines, dashboards, and monitoring systems.

What You’ll Work On

• Design and operate robust ML job execution frameworks for training, inference, and post-processing.
• Build and maintain internal API servers and developer tools to orchestrate ML jobs on Kubernetes (via Argo Workflows, Helm, Terraform).
• Architect, implement, and scale batch (Spark) pipelines for ML training and evaluation.
• Design and monitor data infrastructure using PostgreSQL and other databases.
• Ensure high availability and observability through monitoring tools like Prometheus and Grafana.
• Create internal tools and services to simplify ML experimentation and production workflows.
• Collaborate closely with ML scientists to turn research outputs into user-facing product features
• Partner with engineers, PMs, and other cross-functional teams to deliver high-quality AI products

What We’re Looking For (Minimum)

• Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s degree preferred)
• 3+ years of practical experience in ML platform engineering, MLOps, or data infrastructure. Includes deploying enterprise-grade ML systems (e.g., model serving, pipeline automation), integrating data sources, and building dashboards.
• Proficiency in at least one programming language such as Python, Java, or Go, along with solid understanding of data structures and algorithms
• Experience in cross-functional collaboration and leading projects 
• Impact-driven mindset, strong analytical and problem-solving skills, and a continuous passion for learning cutting-edge technologies.
• Proficient in using LLM-powered tools (e.g., Github Copilot, ChatGPT) to boost development productivity

What We’re Looking For (Preferred)

• Industry experience in the MarTech domain, with a strong passion for building customer-centric products
• Strong ownership mindset and architectural thinking, with the ability to lead cross-functional platform initiatives
• Understanding of core ML and deep learning concepts
• Hands-on experience with end-to-end ML workflows and AI system architecture, and familiarity with platforms like Kubeflow, MLflow, or Apache Submarine.
• Familiarity with distributed computing frameworks (e.g., Apache Spark)
• Proficiency with cloud-native ecosystems (e.g., Kubernetes, Helm, Prometheus, Argo Workflows)

#LI-AK1

About Appier

Appier is a leading SaaS company empowering businesses with cutting-edge artificial intelligence (AI) to drive smarter decision-making. Founded in 2012 with a mission to democratize AI, we transform complex data into actionable insights, making AI accessible and profitable. With 17 offices across APAC, Europe, and the U.S., and listed on the Tokyo Stock Exchange (Ticker: 4180), Appier is at the forefront of AI innovation. Visit www.appier.com for more information.

About the Role

We’re on the lookout for an ambitious and technically outstanding Senior Software Engineer, Machine Learning to join our Enterprise Solution Engineering Team, AIXON. This elite team leverages state-of-the-art ML technologies to solve real-world marketing challenges by integrating omnichannel customer data at scale.

In this role, you’ll be the vital bridge between cutting-edge research and production-grade deployment. You’ll design, build, and optimize scalable, high-performance ML infrastructure—including data pipelines, APIs, monitoring systems, and workflow orchestration—that power transformative AI solutions.

What You’ll Do

• Architect and operate resilient ML job execution frameworks covering training, inference, and post-processing workflows.

• Develop and maintain API services and developer tooling to orchestrate ML workflows on Kubernetes using Argo Workflows, Helm, Terraform.

• Build scalable, efficient batch pipelines with Apache Spark to support large-scale ML training and evaluation.

• Design and maintain robust data infrastructures using Trino, Databricks and other modern database technologies, monitored with Prometheus and Grafana for high availability and observability.

• Develop tooling that streamlines ML experimentation, accelerates production workflows, and empowers cross-functional teams to innovate rapidly.

• Collaborate deeply with ML scientists to transform research prototypes into reliable, scalable, user-facing AI products.

• Lead cloud infrastructure design and operations on GCP, leveraging managed services such as Google Compute Engine (GCE) , Google Kubernetes Engine (GKE) , Cloud Storage, Cloud Functions, Cloud Pub/Sub, Cloud SQL, BigQuery, and more.

• Define and implement CI/CD pipelines with tools like Jenkins, Github Action, or ArgoCD to enable seamless, automated deployments.

• Harness distributed computing and parallel programming principles to optimize system resource utilization and performance.

What You Bring

• Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s degree preferred).

• 5+ years of hands-on experience in ML platform engineering, MLOps, or data infrastructure, deploying enterprise-grade machine learning systems at scale.

• Expert proficiency in Python, Java, or Go, with solid foundations in data structures and algorithm design.

• In-depth experience with cloud environments (AWS or GCP) and cloud-native service management.

• Proven mastery of Docker containers and Kubernetes cluster management, including resource provisioning, autoscaling, and deployment best practices.

• Strong understanding of the ML lifecycle—from training and prediction to evaluation, backtesting, and feedback loops.

• Familiarity with Git workflows and Linux-based development environments.

• Passionate about continual learning and innovation, leveraging AI-powered developer tools like GitHub Copilot and ChatGPT to boost productivity.

What Will Set You Apart

• Experience in the MarTech industry or other customer-centric domains, eager to deliver products that delight users and drive business impact.

• Demonstrated architectural leadership and ownership, skillfully driving complex, cross-team platform initiatives.

• Strong grasp of deep learning fundamentals and end-to-end ML workflow platforms such as Kubeflow, MLflow, or AWS SageMaker.

• Hands-on experience with distributed data processing frameworks like Apache Spark, and pipeline orchestration tools such as Apache Airflow, Argo Workflow, or Luigi.

• Expertise in production-level ML applications, including handling data imbalance, preventing data leakage, and optimizing resource consumption for large-scale training and serving.

• Familiarity with real-time online inference architectures and batch processing trade-offs.

• Enthusiastic adopter of “vibe coding” culture—collaborative, transparent, and always pushing technical excellence together.

• Prior experience building or developing applications related to large language models (LLM), multi-agent LLM systems, or natural language processing (NLP).

Why Join Appier?

At Appier, you’ll stand at the frontier of AI innovation, working alongside world-class engineers and researchers to create products that transform entire industries. Here, your engineering expertise will directly impact millions of users and drive revolutionary advances in marketing technology. If you’re ready to tackle challenging ML infrastructure problems with passion and creativity, Appier is your ultimate playground!

#LI-AK1