2 months ago
Taipei, TaiwanMid Level
Responsibilities
- Build and operate reliable, scalable ML and generative-AI pipelines for automated content creation, personalization, and optimization across creative formats and products.
- Productionize research prototypes and models through service and API contracts, containerized workers, asynchronous orchestration, artifact storage, and clear ownership boundaries.
- Apply LLMs, VLMs, multimodal generation, and agentic tool use to improve creative quality, automation, and personalization.
- Engineer 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 using logs, traces, evaluations, and experiments.
- Collaborate with scientists, backend and frontend engineers, product managers, and designers to translate business needs into maintainable ML systems and ship iteratively.
Requirements
- 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 maintainable, tested code.
- Strong Python skills and hands-on experience with PyTorch, TensorFlow, or equivalent machine-learning tooling.
- Experience taking models, prompts, or data workflows from prototype to production using APIs, containers, batch or stream processing, and cloud infrastructure.
- Experience with tooling such as Docker, Kubernetes, GCP, workflow orchestrators, message queues, Spark, or OpenTelemetry.
- Understanding of production ML 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, including responsible use and validation of AI-assisted development tools.
- Preferred experience with generative media, creative optimization, advertising or MarTech, recommendation, or content-generation products.
- Preferred experience designing agent tools, feedback-loop pipelines, rigorous evaluations, online experiments, or multimodal systems such as LLMs, VLMs, image or video generation, RAG, tool use, or agent frameworks.
Tech Stack
Categories
About Appier
Appier builds AI-driven advertising and marketing software for enterprises, offering programmatic ad bidding, customer data, and predictive tools delivered as SaaS. Founded in 2012 and headquartered in Taipei, it is a public company listed on the Tokyo Stock Exchange (ticker: 4180). Its Ad Cloud processes millions of bid requests per second across APAC, Europe, and the U.S., and is used by in-house and agency marketers.