AI Engineer, Playable Ads
Salary not listed • Taipei, Taiwan • Full-time
Posted 2mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Taipei, Taiwan
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Appier is seeking a Machine Learning Engineer for their Playable Ads team to transform promising AI models and prototypes into reliable, scalable creative-generation systems for real-world products. This role blends applied machine learning with software engineering, requiring collaboration with scientists, engineers, product managers, and designers to build and improve ad creative generation pipelines. You will focus on ensuring these systems meet production standards for quality, latency, cost, observability, and reliability, ultimately driving higher engagement and conversion for apps and games worldwide.
Responsibilities
- Build and operate scalable ML and generative AI pipelines for automated content creation, personalization, and optimization.
- Productionize research prototypes and models by designing service contracts, containerized workers, and orchestration.
- Apply ML techniques like LLMs, VLMs, multimodal generation, and agentic tool use to enhance creative quality and automation.
- Engineer reliable workflows with schema validation, idempotency, retries, and end-to-end testing.
- Define and monitor key metrics (quality, latency, failure-rate, cost) and use data to diagnose and improve outcomes.
- Collaborate with cross-functional teams 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 equivalent practical experience.
- 3+ years of experience building production software or ML systems with strong CS fundamentals and tested code.
- Proficient in Python and experienced with PyTorch, TensorFlow, or similar ML tooling.
- Experience taking models, prompts, or data workflows from prototype to production using APIs, containers, and cloud infrastructure.
- Familiarity with cloud and ML platform tools like Docker, Kubernetes, GCP, CI/CD, workflow orchestrators, message queues, Spark, or OpenTelemetry.
- Understanding of production ML concerns: reproducibility, versioning, evaluation, monitoring, failure handling, latency, and cost.
- Strong debugging and systems-thinking skills across distributed components.
- Clear communication and cross-functional collaboration skills.
- Experience with generative AI or multimodal systems (LLMs, VLMs, image/video generation, RAG, tool use, agent frameworks) is preferred.
- Experience with generative media, creative optimization, advertising, or MarTech products is preferred.