AI & Deep Learning Scientists

TED Optimus Sdn; Kuala Lumpur; On-site; MYR 80,000 - 120,000; Full time

Job summary

TED Optimus Sdn in Kuala Lumpur is seeking experts in Large Language Models and deep learning. You will design and train models that convert massive market data into valuable trading signals.

The role requires strong expertise in deep learning and experience with large-scale data pipelines. You'll collaborate with researchers to enhance predictive models and push our GPU infrastructure to its limits.

Ideal candidates are deeply curious, rigorous in their approach, and thrive in fast-paced environments.

Qualifications

• Strong background in deep learning, with hands-on experience training large neural networks.
• Expertise in LLMs, MoE architectures, and/or time-series forecasting.
• Proficiency in Python and a modern deep learning framework (PyTorch or JAX).
• Experience with distributed/multi-GPU training and large-scale data pipelines.

Responsibilities

• Design, train and fine-tune large-scale deep learning models for financial forecasting.
• Push GPU cluster limits through efficient distributed training.
• Translate raw market data into robust predictive features.

Skills

Deep learning expertise

Experience with LLMs

Proficiency in Python

Distributed training knowledge

Familiarity with Git

Job description

Squeeze the absolute potential out of our GPU cluster. We are looking for experts in Large Language Models (LLMs), MoE architectures, and time-series forecasting to transform raw data into alpha.

As part of Axiom Quant, you will sit at the frontier of applied AI research, designing and training the models that turn massive, noisy market data into precise, commercially valuable trading signals.

Responsibilities

• Design, train and fine-tune large-scale deep learning models (LLMs, MoE architectures) for financial and time-series forecasting
• Push our GPU cluster to its limits through efficient distributed training and inference
• Translate raw, multi-source market and alternative data into robust predictive features and alpha signals
• Run rigorous experiments, ablations and back-tests to validate model performance and robustness
• Collaborate with quant researchers and engineers to bring research models into production

Requirements

• Strong background in deep learning, with hands-on experience training large neural networks
• Expertise in LLMs, MoE architectures, and/or time-series forecasting
• Proficiency in Python and a modern deep learning framework (PyTorch or JAX)
• Experience with distributed/multi-GPU training and large-scale data pipelines
• Comfortable using Git

Qualities

• Deeply curious and driven to extract every last drop of performance from data and hardware
• Rigorous and scientific - lets evidence, not intuition, decide
• Thrives in a fast-paced, ambiguous research environment
• Strong sense of ownership over models from research to production