Skip to content
CV Rocket
Get started
How it worksThe enginePricingBlogContactSign in

Open nowPosted today

Research Scientist - End-to-End Generative Search Technology Based on Pre-Trained Foundation Models - Global Frontier Tech Recruitment Program - 2027 Start (PhD) (Singapore)

BYTEDANCE PTE. LTD.

Pay
SGD 12,000 – SGD 19,500 a Monthly
Where
Central, Singapore
Get the CV for this job

From $25 per CV, paid once. No subscription.

Your applicationOpen nowResearch Scientist - End-to-End Generative Search Technology Based on Pre-Trained Foundation Models - Global Frontier Tech Recruitment Program - 2027 Start (PhD) (Singapore)BYTEDANCE PTE. LTD. · Central, Singapore

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on BYTEDANCE PTE. LTD.'s own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: posted today

The posting

Team Introduction

Our TikTok Search Team is responsible for building and owning our search engine which provides our users the best search experience. On the Search Team, you'll have the opportunity to build a full-stack search engine system and combine information retrieval technology with modern machine learning methods from related fields such as NLP, Computer Vision, Multimodal, and Recommender Systems. We embrace a culture of self-direction, intellectual curiosity, openness, and problem-solving.

Topic Content: With the rapid advancement of foundation model technology, the field of AI-powered search is encountering new opportunities and challenges. Traditional search technologies have begun to reveal significant limitations when confronted with massive data volumes, multimodal information, and complex multi-turn user needs. It is therefore necessary to leverage foundation models to build next-generation AI search systems, enhancing the intelligence of search systems and optimizing user experience. Specific objectives include:

1. Explore the integration of foundation models with ranking algorithms to improve personalized ranking accuracy and user experience.

2. Explore end-to-end generative search models based on multimodal pre-training.

3. Explore LLM-based agent technology to improve user satisfaction under complex ambiguous queries and multi-turn search scenarios.

Topic Challenges:

1. Personalized ranking: Traditional ranking algorithms struggle to fully leverage multimodal information, and their limited model complexity fails to meet user demands for precise and personalized search.

2. Ultra-large-scale retrieval and ranking: Traditional discriminative cascaded ranking systems cannot meet the efficiency requirements for retrieval and ranking across hundred-billion-scale candidate pools.

3. Increasingly complex search needs: User search needs are growing increasingly complex. Traditional search frameworks struggle to accurately understand the semantics of long, complex, and ambiguous queries in multi-turn conversations, resulting in low search result satisfaction.

Topic Value:

1. Technical value: Break through the bottlenecks of traditional search technology; build a next-generation AI search architecture driven by LLM agents; and address industry challenges including personalized ranking, ultra-large-scale retrieval and ranking, and understanding and fulfilling complex search needs.

2. Business value: Significantly improve search user experience and satisfaction, driving improvements in search LT and users' proactive search intent.

Minimum Qualifications:

1. Individuals who are completing or recently completed a PhD in AI, Computer Science, Computer Engineering, or a related technical discipline.

2. Excellent coding skills and a solid foundation in Machine Learning/Deep Learning/LLM, proficient in C/C++ or Python

Preferred Qualifications:

1. Outstanding ability to define, analyze, and solve problems; candidates with publications at AI conferences such as AAAI, NeurIPS, ACL, etc., or in-depth research experience are preferred.

2. Strong resilience, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.

Posting text belongs to the employer. Removal requests: contact us.

From $25, paid onceGet the CV for this job

What happens when you press

One press. We do the rest.

  1. A CV for this posting

    Written against BYTEDANCE PTE. LTD.'s own wording, from every piece of relevant proof in your profile.

  2. 25 readers review it

    Recruiter, hiring manager, skeptic and more read every draft, round after round. You get the best round.

    The review screen in CV Rocket: how each CV was read, round by round.

  3. We apply on BYTEDANCE PTE. LTD.'s form

    Our application engine gets through the hardest forms there are. Where a question needs you, AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

    An application in CV Rocket: every answer filled in on the employer's form.

  4. Every reply, sorted

    BYTEDANCE PTE. LTD.'s answer lands in your private mailbox, and we classify it on arrival: interview, question, rejection.

    The CV Rocket inbox: each employer reply classified as an interview, an action or a rejection.

  5. Reply with AI

    AI helps you write the email, checks it and sends it. We show you whether the recruiter read it.

  6. The interview in your calendar

    Full integration with your calendar. The invitation goes straight in.

    An interview invitation in the CV Rocket inbox, added to the candidate's calendar.
Get the CV for this job

From $25 per CV, paid once. No subscription.

Why it works

3×

more interviews than doing it yourself with ChatGPT.

ChatGPT writes a CV and never learns what happened to it. We see every reply. For each CV we know:

  • How it was written, and how the review scored it
  • When we applied, and how long after the posting went up
  • Which posting, which company, which city
  • Who got the interview, and who heard nothing

That is how we know which CVs get called.

Get the CV for this job

From $25 per CV, paid once. No subscription.

The numbers game

More applications. More interviews.

Every application goes out with its own CV, written for that posting and paid once. Send enough of them and the law of large numbers finds you the job.

Get the CV for this job
By hand5–10
With CV Rocket100
applications a day

Before you press

Straight answers

Get the CV for this job

From $25 per CV, paid once. No subscription.

What if my background isn't good enough?

We make the most of the background you have. The CV uses every piece of relevant proof your profile holds, and one of the 25 readers reads your whole profile and flags what the CV left out.

Do you really apply for me?

Yes, on the employer's own form, the hardest ones included. Where a question needs you, you answer it right there and AI suggests the best answer. Don't want us applying from our IP addresses? Use our Chrome extension: we apply straight from your own browser.

Is it a subscription?

No. You pay once per CV, from $25. Every application goes out with its own CV, written for that posting.

One job. One CV.
Paid once.

Pick the posting you want. We write for it, apply for you and catch the reply.

Get the CV for this job

From $25 per CV, paid once. No subscription.