本職缺目前暫停接受應徵。

(Archived) Machine Learning Engineer (Generative Models)

儲存
職缺 23 天前更新

職缺描述

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, to drive impact.

Please note that only applications through the link will be considered

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.
  • 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.

職務需求

Requirements and skills- Technical

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

Requirements and skills- 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.

面試流程

There are three main stages in our interview process:

  1. Screen calls (1-2 calls)
  2. Take-home quiz & review
  3. Onsite interview (3-4 hours visiting)

If we see a fit with the company, we will reach out to start getting to know each other. You can expect traditional discussions as well as participating situational exercises. The goal of the interview process will be for us to see your skills and let you get to know the team and work culture to see if we are a match.

Please note that only applications through the link will be considered

查看所有職缺
查看所有職缺
儲存
1
需具備 5 年以上工作經驗
1,100,000 ~ 1,700,000 TWD / 年
選擇性或彈性遠端工作
您的邀請連結
這是您專屬的職缺邀請連結。當有人透過您的邀請連結應徵這個職缺時,您會收到 email 通知。
分享職缺
應徵此職缺的人也應徵了
Logo of PicCollage 拼貼趣.

關於我們

嗨!我們是來自矽谷的新創團隊 PicCollage Company,我們團隊鼓勵自由創造,擁抱差異與多元文化,打造『開放、學習、分享』的工作環境。產品開發近期以『Creative AI』為主軸,打造使用者能輕鬆發揮創意的各項影像編輯產品。

『Creative AI』 是創意 AI 的展現和延續

透過 Generative AI 和 ML 技術的研究,開發出具創意的 AI 並導入產品功能。Creative AI 不僅展現在我們挑選的技術領域,還有主打的產品開發類別,讓使用者在操作 app 的過程中展現更多創意!

主要產品『PicCollage 拼貼趣』是一個追求自由創造及分享的影像拼貼 app,主要使用者來自美國、英國及日本等國家。目前全球下載量已突破 2.6 億,每個月 1500 萬穩定用戶量,並穩定增長中。

另外,新產品團隊 Explore 持續開發新 app 產品,像是挑選片段就能產出的對拍影片 (OnBeat)、或是一張頭像就能生成你的專屬迷因 (MemeMe)、同時線上多組討論視訊軟體 (MixerChat) 等。再戰創意領域 app 的下一個產品里程碑!


我們相信:

  • Always be Learning - 除了發放學習補助金之外,我們也會特別安排時間讓團隊學習不同事物,可以正面的回饋在個人發展及團隊目標!
    • Workshop Day ➤ https://youtu.be/6N6L7KbajII
    • 定期舉辦的 PicCollage 黑客松 : InnoFest ➤ https://youtu.be/kbMPoQ6ifmc
  • Live Well, Work Well - 提供彈性的休假制度及工作模式,讓成員可以更好的安排生活,並在工作上有最傑出的表現!
    • Life at PicCollage ➤ https://www.youtube.com/@piccollageteam4227/featured
  • Win as Team - 重視每位成員表達意見及想法的機會,不論來自哪個部門,都可以為團隊目標盡一份心力!

期待遇見更多 #builders #dreamers #thinkers 來激盪創意,加入 PicCollage Company 和我們一起做出好玩、富有創意的軟體產品到全世界🌎


我們提供部分遠端工作:彈性工作地點 (需配合台灣時區參與會議)

欲更認識我們,歡迎參考以下連結:

  • Our Office ➤ https://www.youtube.com/watch?v=R7jqZdIugRs
  • Our Products ➤ https://picc.co/products
  • Tech Blog ➤ https://tech.pic-collage.com/
  • 成員分享:團隊文化 ➤ https://bit.ly/3zyQrAl

想更認識 PicCollage 團隊和職缺嗎? 來參加每個月舉辦的 Online Info Session 線上招募同樂會,你能更了解招募中的職缺、並與成員互動 QA唷。現在就報名登記:https://forms.gle/bkHk4QUPC4ACCnMcA



職缺

全職
中高階
1
210萬 ~ 350萬 TWD / 年
儲存

實習生
實習
1
250 ~ 280 TWD / 小時
儲存

全職
中高階
1
200萬 ~ 300萬 TWD / 年
儲存