Large Language Model Intern

Razer Inc.; Singapore; On-site; SGD 17,000 - 23,000; Full time

Job summary

Razer Inc. seeks an internship candidate to work on training and optimizing language models for Razer Software, spanning pretraining to deployment. You will join senior engineers to own end-to-end workstreams from design to analysis.

Expect hands-on experience with LLM training, data engineering, and model compression. This role emphasizes practical ML experimentation and collaboration in a fast-paced, gaming-centered environment.

Qualifications

• Education in CS/AI/Data Science; current student (Bachelor/Master/PhD).
• Must-have knowledge: LLM training paradigms; transformer fundamentals; latency/memory tradeoffs.

Responsibilities

• Train and optimize language models for Razer Software across the full pipeline.
• Handle corpus construction, fine-tuning (SFT, LoRA), evaluation, and deployment optimization.
• Collaborate with senior data scientists and engineers on end-to-end workstreams.
• Benchmark quality, latency, and memory under production constraints.

Skills

Python

PyTorch

Experiment design

Data engineering

Education

CS/AI/Data Science degree

Tools

Hugging Face

Accelerate

DeepSpeed

Linux

Job description

Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.

Job Responsibilities:

Train and optimize language models for Razer Software, across the full pipeline — pretraining corpus construction, fine-tuning, evaluation, and deployment optimization.

Job Scope

You'll work with senior data scientists and engineers on the model powering Razer Software, covering the full training pipeline:

• Corpus construction — cleaning, filtering, deduplication, and mixing of training data

• Fine-tuning — SFT, LoRA, and preference optimization runs

• Evaluation — quality, latency, and memory benchmarking against production constraints

• Deployment optimization — compression and quantization experiments for on-device targets

You’ll own discrete workstreams end-to-end (design → run → debug → analyze → iterate) rather than executing isolated tasks handed down by a mentor.

Learning Objectives

By the end of this internship, you will have:

• Hands‑on experience across the full LLM training lifecycle at production scale — not toy datasets

• Practical judgment in data engineering: how cleaning/filtering/mixing decisions propagate into model quality

• The ability to independently run a rigorous ML experiment loop — hypothesize, train, evaluate, diagnose subtle regressions, iterate

• Direct exposure to how deployment constraints, shape training and architecture decisions, including compression, quantization, and distillation tradeoffs

• Mentorship from senior engineers and visibility into how a production roadmap for a shipping AI feature actually gets decided.

Candidate Requirements

• Education: Current Bachelor's, Master's, or PhD student in CS, AI, Data Science, or a related field

• Must-have knowledge: LLM training paradigms (pretraining, SFT, LoRA, preference optimization); Transformer/deep learning fundamentals; how deployment constraints (latency, memory) shape training decisions

• Must-have skills: Python; hands‑on PyTorch model training; data engineering for training corpora (cleaning, filtering, dedup, mixing); end-to-end experiment running (debugging, hyperparameter tuning, analysis); benchmarking quality/latency/memory

• Nice‑to‑have: Model compression (quantization, distillation), distributed training (multi‑GPU/parallelism), Hugging Face/Accelerate/DeepSpeed, Linux environment

• Screening bar (hard requirement): Demonstrable hands‑on model training/fine‑tuning experience (coursework, research, internship, or open source). API‑calling or prompt‑engineering‑only experience does not qualify.

• Strong positives: Trained a model from scratch (any scale), multi‑GPU training, model compression/on‑device deployment work, top‑tier publications (CVPR, NeurIPS, ICML, ACL, ICLR, EMNLP, etc.)

Pre-Requisites :

Razer is proud to be an Equal Opportunity Employer. We believe that diverse teams drive better ideas, better products, and a stronger culture. We are committed to providing an inclusive, respectful, and fair workplace for every employee across all the countries we operate in. We do not discriminate on the basis of race, ethnicity, colour, nationality, ancestry, religion, age, sex, sexual orientation, gender identity or expression, disability, marital status, or any other characteristic protected under local laws. Where needed, we provide reasonable accommodations - including for disability or religious practices - to ensure every team member can perform and contribute at their best.

Are you game?