Machine Learning Platform Engineer

About Us:We are a profitable and growing company, originating in Silicon Valley and now headquartered in Taiwan. We combine intuitive design with Creative AI tech to create inspiring products for millions of people worldwide.
We offer a fun, creative, and international workplace with competitive compensation, stock options, flexible hybrid work, free lunch, and more.
Ready to make a big impact with a talented team? Come create with us.

About The Role:As a Machine Learning Platform Engineer, you will play a key role in designing, implementing, and maintaining robust machine learning platforms and data pipelines, ensuring smooth deployment, scaling, and monitoring of models in production. You will drive automation to accelerate the development, evaluation, and integration of machine learning models, improving collaboration and overall efficiency. In addition to optimizing production environments, you will act as a bridge between machine learning developers and software engineers, ensuring seamless integration of ML systems into applications. You will also share best practices for MLOps and have the opportunity to work on high-impact projects that reach millions of users, as well as help bring innovative new applications to market.

What You'll Do:

• Design, implement and maintain machine learning platform and data pipelines, ensuring seamless deployment, scaling, and monitoring of models in production environments.
• Set up monitoring systems for deployed models and tracking key metrics.
• Apply and share software engineering best practices within the context of machine learning.
• Collaborate with ML developers to ensure model performance is maintained in production and work with software engineers to integrate ML systems into the broader application stack.
• Accelerate machine learning development, evaluation, and integration speed through automation of workflows, tools, and processes to enhance collaboration and efficiency.

Skills We’re Looking For:

• Strong programming skills in Python.
• Proficiency with containerization tools (Docker, Kubernetes) and cloud platforms (GCP, AWS, Azure; expertise in at least one).
• Experience working with backend servers and APIs (e.g., FastAPI, Django, or similar frameworks).
• Deep knowledge of the entire ML workflow.

Nice-To-Haves:

• Experience with machine learning frameworks (PyTorch, TensorFlow).
• Experience with MLOps tools (Kubeflow, MLflow, TFX).
• Experience with monitoring tools (Prometheus, Grafana) and logging frameworks.
• Knowledge of data engineering concepts (ETL pipelines, data lakes, data warehouses).