Machine Learning Research Engineer- SOUTH AFRICA (REMOTE)

Our client is looking for a highly capable Machine Learning Research Engineer to join the AI R&D team and work on the development and optimization of next-generation language models for specialized AI applications.

You will work across model training, fine-tuning, post-training, data development, evaluation, and inference optimization. This is a hands-on research engineering role for someone who enjoys running experiments, understanding why models behave the way they do, and turning research ideas into working models.

What You will Do

• Fine-tune and post-train open-source language models.
• Experiment with supervised fine-tuning, continued pretraining, distillation, preference optimization, and reinforcement learning.
• Develop high-quality training datasets and synthetic data pipelines.
• Generate and curate high-quality reasoning and instruction datasets.
• Experiment with different model architectures, training strategies, and hyperparameters.
• Build rigorous evaluation benchmarks to measure model capabilities.
• Analyze model failures and identify opportunities for improvement.
• Optimize models for inference latency, memory usage, throughput, and cost.
• Experiment with models ranging from hundreds of millions to several billion parameters.
• Research techniques for transferring capabilities from larger models into smaller models.
• Work closely with AI systems engineers to integrate models into production AI systems.
• Reproduce relevant academic research and translate promising ideas into experiments.
• Maintain clear experiment tracking, documentation, and reproducible training pipelines.

Required Experience

• Strong experience with PyTorch and modern ML frameworks.
• Hands-on experience fine-tuning or training language models.
• Strong understanding of transformer architectures.
• Experience with Hugging Face Transformers and related tooling.
• Experience with SFT, LoRA/QLoRA, knowledge distillation, or other post-training methods.
• Strong Python programming skills.
• Experience preparing and processing large-scale datasets.
• Strong understanding of model evaluation and benchmarking.
• Ability to design controlled experiments and interpret results.

Nice to Have

• Experience with models under 7B parameters.
• Experience with continued pretraining.
• Experience with DPO, GRPO, RLHF or related methods.
• Experience generating synthetic training data.
• Experience with coding models or developer-focused AI.
• Experience with distributed training, FSDP, DeepSpeed or similar.
• Experience with model quantization and inference optimization.
• Publications, research projects, or meaningful open-source contributions.

What We are Looking For

We care less about titles and more about what you have actually built.

If you have taken an open model, trained it on a new dataset, improved its capabilities, diagnosed why it failed, and iterated until you got a measurable improvement, we want to hear from you.