Applied Machine Learning Engineer
Engineering5+ yearsRemote
Run BiOS gives enterprises one path from approved data to a trained, evaluated and deployed model. As an Applied Machine Learning Engineer, you own that path from dataset design and method selection through evaluation, deployment and continuous improvement.
What you'll do
- 01Own fine-tuning engagements end to end: dataset inspection, method selection, run configuration, evaluation and handoff.
- 02Build internal tooling for dataset validation, training orchestration and model evaluation across a catalog of 250k+ open models.
- 03Debug the weird ones: loss spikes, adapter pathologies, tokenization edge cases, VRAM ceilings.
- 04Feed recurring customer patterns back to product and research as concrete, prioritized proposals.
- 05Help customers serve their fine-tuned weights through our inference stack.
What you bring
- At least 5 years applying machine learning in production environments.
- Real experience fine-tuning LLMs or vision-language models: configuring runs, reading curves, not only calling APIs.
- Strong Python and PyTorch, and comfortable reasoning about GPU memory when things get tight.
- Data instincts: you look at the dataset before you touch the model.
- Clear communication: you can explain a failed run to a customer without hand-waving.
Nice to have
- Breadth across model families: DeepSeek, Qwen, Kimi, GLM, Gemma and friends.
- Depth in the Hugging Face ecosystem: transformers, PEFT, TRL, datasets.
- Multi-GPU training frameworks and checkpoint management.
- Serving experience: vLLM or similar inference stacks.