// Open Role at Razer
Join Razer as an LLM Intern to train and optimize advanced language models for Razer Synapse, gaining hands-on experience in the full model lifecycle from data engineering to deployment optimization at a global gaming company.
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.
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 Synapse's device assistant, 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 (on-device latency and memory on Razer Synapse hardware) 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.)
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?
Current Bachelor's, Master's, or PhD student in CS, AI, Data Science, or related field. Must have knowledge of LLM training paradigms (pretraining, SFT, LoRA, preference optimization), Transformer/deep learning fundamentals, and how deployment constraints shape training decisions. Must have hands-on PyTorch model training, data engineering for training corpora (cleaning, filtering, dedup, mixing), end-to-end experiment running (debugging, hyperparameter tuning, analysis), and benchmarking skills. Demonstrable hands-on model training/fine-tuning experience (coursework, research, internship, or open source) is a hard requirement.