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.