Applied Machine Learning Engineer

Role overview Help customers fine-tune open language and vision-language models, from inspecting their data through evaluating and serving trained weights. The work combines hands-on ML problem solving with building internal tools that make training workflows more reliable and turn recurring customer needs into platform improvements.

Responsibilities - Lead fine-tuning work from dataset review and method choice through run configuration, evaluation, and handoff. - Build tools for dataset checks, training orchestration, and model evaluation across a large open-model catalog. - Investigate training and deployment issues, including unstable loss, adapter behavior, tokenization edge cases, and GPU memory limits. - Identify recurring customer needs and propose prioritized improvements to product and research teams. - Help customers deploy fine-tuned model weights through the inference stack.

Requirements - At least five years applying machine learning in production settings. - Practical experience fine-tuning LLMs or vision-language models, including configuring runs and interpreting training curves. - Strong Python and PyTorch skills, with the ability to reason about constrained GPU memory. - A data-first approach that includes inspecting datasets before selecting or adjusting models. - Clear communication, including explaining failed runs and their causes to customers.