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

Open nowPosted today

Research Scientist (AutoResearch on LLM)

MyCareersFuture97,045 open roles

Pay
SGD 7,000 – SGD 20,000 a month
Where
Central, Singapore
Get the CV for this job

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

Your applicationOpen nowResearch Scientist (AutoResearch on LLM)MyCareersFuture · 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 MyCareersFuture'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.

8.1% of postings close within 7 days. Measured by our own scanner across the market. MyCareersFuture postings stay open a median of 3 days.

Share of postings closed within
  1. 1.7%1 day
  2. 3.6%3 days
  3. 8.1%7 days
  4. 15.1%14 days
  5. 34.0%30 days
This job: posted today

MyCareersFuture median: 3 days open

The posting

Our client is a Singapore-based automotive tech company, building AI-powered computing platforms for software-defined vehicles. They specialize in intelligent cockpit systems and autonomous driving computation, combining custom automotive SoCs with advanced software to help automakers deliver safer, smarter, more personalized in-car experiences.

Research Scientist

Role Overview:

As a Research Scientist (AutoResearch on LLM), you will focus on cutting edge AI-Driven AI development using Autoresearch(Automated Machine Learning) for Large Language Models (LLM). You will design and deploy automated research frameworks that autonomously discover. optimize, and train the next-generation LLM architectures. You will bridge the gap between hardware constraints and model design ensuring that the automatically optimized architectures achieve or exceed the pre-training efficiency and downstream performance of state of the art established base models

Key Responsibilities:

· Design, build and scale automated search and optimization systems (Such as Neural Architecture Search, evolutionary algorithms, or LLM-driven research agents) to autonomously discover optimal LLM architectures

· Analyze and integrate hardware level constraints (e.g. tensor parallel limits, memory bandwidths, latency, cache hierarchies, FLOPs, and energy efficiency of target silicon) into the optimization loop

· Scale up discovered architectures to perform large-scale pre-training. Ensure the final models meet to exceed the performance, training efficiency, and convergence rate of existing top-tier foundation models

· Develop accurate, cost effective proxy task, evaluation protocols, and scaling laws to predict full-scale LLM performance from early-stage automated search limits

· Collaborate closely with chip architects, system/compiler engineers, and foundation model researchers to co-optimize hardware execution efficiency and model training algorithms

· Stay at the forefront of AutoML, hardware-software co-design, and LLM pre-training literature, contributing to peer-reviewed publications and IP generation where applicable

Qualifications & Requirements:

· Master's or PhD degree in Computer Science, Electrical Engineering, Applied Mathematics, or a related quantitative field with a focus on Deep Learning

· Strong research background in LLM pre-training, Transformer architectures, and scaling dynamics

· Hands-on experience with AutoResearch, AutoML, automated optimization algorithms, or using AI agents/LLMs for automated scientific discovery

· Solid understanding of GPU/accelerator architectures, memory hierarchies, parallel training strategies(tensor, pipeline, data parallel) and hardware performance profiling

· Production grade coding skills in Python and deep learning frameworks (e.g. Pytorch, JAX, Megatron-LM, Deepspeed).

· Demonstrated experience in training, scaling and evaluating large scaling models from scratch

· Having first author publications in top tier machine learning or system conferences (e.g. NeurIPS, ICML, ICLR, ASPLOS, ISCA, MLSys) is preferred

· Experience writing custom kernels (e.g. Triton, CUDA) or working with machine learning compilers are preferred

· Direct experience working with silicon/chip design teams to customize model architectures for specific ASIC/GPU/FPGA constraints.

· Experience building self improving AI Loops or automated coding research assistants

EA Personnel Registration No: R1106329

EA License No: 12C6254

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 MyCareersFuture'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 MyCareersFuture'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

    MyCareersFuture'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

Nearby

Live postings like this one

Same employer first, then the same role elsewhere.

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.