Machine Learning Engineer (Generative Models)

This is an exciting role that offers a chance to work at the cutting edge of AI. In short, your main responsibility would be to harness the potential of recent breakthroughs in AI, especially in the area of generative models (text to image and large language models), to drive impact.

Responsibilities:

• Understand recent advancements in machine learning, especially Generative AI and Large Language Models (LLMs), at a deep technical level. Use this knowledge to create novel technologies that were not possible before.
• Keep up to date with developments in other areas like multi-modal models, computer vision, model distillation and fine-tuning. Some examples are trying out Segment Anything Model, Llama LLM, how to build something like LangChain.
• Identify key problems and opportunities that can be addressed using these technologies.
• Brainstorm and implement possible improvements to these technologies. Eg how to make a model smaller, optimize it for CoreML, and finetune for new data.
• Stay up to date with the research landscape in these areas.

Requirements

Technical (Required)

• Well-versed with using at least one deep learning framework, preferably PyTorch.
• Clear and thorough understanding of the original transformer architecture and its variants.
• Strong understanding of how multi-modal and large language models work (eg data used for training, loss functions used, how to finetune etc).
• Have a keen eye for detail - willingness to understand the nitty-gritty details, reading the source code when necessary.
• Good foundational knowledge of probability and linear algebra.

Non-technical

• Ability to think about potential applications and the impact of recent advances in AI (and not just focus on technical details).
• Ability to communicate clearly and effectively.
• Self-motivated and proactive: the ability to learn, figure things out and identify key problems to solve with little or no supervision.

Technical 

• Knowledge of HuggingFace API patterns (used across libraries like diffusers, transformers, accelerate etc).
• Knowledge of docker and kubernetes.
• Knowledge of diffusion models.
• Publications relevant to ML.

Flexible Work Time & Remote Work 彈性上下班時間 & 遠端工作

Maker Time 每週一天 Maker Time，不進行不必要的會議和討論

Team Activities and Workshops 各式活動與學習機會 (技術分享、Design Demo, Hack Days等）

Fun, Creative & International 雙語工作環境，團隊成員來自世界各地

Free Lunch and Dinner in the Office 辦公室內團隊午餐晚餐

Sports Classes in Office/Online 和同事一起起來動一動

PicCafe 健康零食水果，永遠餵飽你的胃

Awesome & Fun Office 有趣舒適大稻埕風的辦公室