Machine Learning Engineer

What you'll do

• Own and improve model-backed de-identification systems, using error analysis, datasets, experiments, and decision-relevant metrics.
• Build active-learning and evaluation systems to find important failures and verify model quality across data segments and failure modes.
• Ship reproducible, observable improvements into production while balancing quality, high-risk coverage, inference cost, latency, and throughput.

What they ask for

• 3+ years of professional machine learning or software engineering experience, including improving models in production.
• Strong Python and software engineering skills, with the ability to work in data pipelines and production systems.
• Experience improving model quality through error analysis, data work, experimentation, implementation, deployment, and iteration.
• Strong grasp of precision, recall, F1, calibration, thresholding, class imbalance, imperfect labels, distribution shift, and representative evaluation.

Nice to have

• Experience with NER, entity resolution, information extraction, document understanding, multimodal systems, or privacy-preserving ML.
• Experience with active learning, uncertainty sampling, weak supervision, human-in-the-loop review, or LLM-assisted evaluation pipelines.

Tools

Python, LLM, GLiNER, ONNX Runtime, TensorRT

AI focus

Develop and evaluate ML and LLM-based methods for de-identifying enterprise data, including named-entity recognition, entity resolution, structured extraction, classification, and document or multimodal understanding.