Senior Software Engineer, Machine Learning (AIXON)

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.

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.

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

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

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

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

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

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

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  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.

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

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

• 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.

• 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).