Research Engineer

Hume AI is looking for a Research Engineer to advance speech-language and audio models through hands-on model development, large-scale training, and rigorous evaluation. Contribute to our endeavor to ensure that AI is guided by human values, the most pivotal challenge (and opportunity) of the 21st century.

About the Role

As a Research Engineer, you will work with our research team to develop audio models and the tools used to study and improve them. You will turn open questions into experiments, train and refine models, and build the software that supports distributed training, inference, and benchmarking. Your work will connect model development with the data and evaluation systems needed to test ideas at scale.

Your work will span models developed in-house at Hume and models developed by our customers. You will collaborate directly with customer research and technical teams to understand their objectives, design research programs, and carry out model training, validation, evaluation, and continued development. Findings from these projects will inform new experiments and improvements to the models and methods we build.

This is a hands-on individual contributor role for someone who enjoys both open-ended investigation and building the code that makes research reproducible and useful. You will own technical work from an initial research question through experiments, analysis, and validated model improvements.

What You’ll Do

• Investigate research questions in speech-language, audio, and multimodal machine learning, and design experiments to test hypotheses about model behavior and capabilities.
• Train, fine-tune, validate, and further develop models built in-house at Hume and models developed by our customers.
• Build reliable software for distributed model training, inference, and benchmarking, making experiments reproducible and results comparable.
• Develop data workflows for collecting, storing, preprocessing, and analyzing large-scale datasets used in model training and evaluation.
• Design evaluation protocols and benchmarks that measure model quality, generalization, robustness, and progress against research objectives.
• Analyze datasets, model outputs, and failure cases; compare training approaches and use the evidence to guide the next round of model development.
• Collaborate with customer research and technical teams to define research questions, understand their models and data, and carry out training, validation, evaluation, and further development.
• Document experimental designs, results, limitations, and technical decisions so Hume colleagues and customer research teams can reproduce and build on the work.

What You’ll Bring

• At least two years of experience contributing to model training or fine-tuning with large-scale text, audio, image, or video datasets, with relevant depth in speech, audio, or multimodal machine learning.
• Strong Python and PyTorch skills, including the ability to develop, debug, and maintain model training and evaluation code.
• Experience writing robust, maintainable code for research systems that operate beyond an initial notebook or prototype.
• Strong experimental judgment: formulating hypotheses, establishing useful baselines, designing evaluations, and interpreting results carefully.
• Evidence of research and model-building ability through relevant publications, model releases, published systems, or substantive open-source contributions.
• Comfort iterating quickly on uncertain research directions and taking ownership of open-ended technical problems.
• Clear written and verbal communication, including the ability to explain methods, findings, and limitations to research colleagues and customer technical teams.

A PhD is not required. We value demonstrated research ability and the quality of the models, experiments, and systems you have helped build.

Bonus Points

• Experience training or fine-tuning transformer models.
• Depth in text-to-speech, automatic speech recognition, speech-to-speech, prosody, emotion recognition, or audio understanding.
• Experience with post-training, preference learning, or human evaluation of generative models.
• Experience improving the efficiency of distributed training, inference, or large-scale data processing.
• Experience collaborating with external research teams on model development or evaluation.