Senior Machine Learning Engineer

6+ years hands-on ML/NLP experience

(or 3+ years post-PhD/Master's) with at least two delivered, revenue-impacting products in production environments

including transformer stacks (BERT/GPT/T5), RAG systems, vector-based information retrieval, and latency/throughput optimization techniques

Proven track record building NER or entity-resolution systems

at 100M+ record scale with experience in record linkage, data deduplication, and knowledge-graph integration

(PyTorch or TensorFlow) paired with software-engineering rigor (Python, Go/Java a plus) and familiarity with embedding models and vector search technologies

Invent and productionize Transformer/RAG/Graph RAG architectures

that surface the right contact, company, or insight while driving quantization, distillation, and SLM fine-tuning (GTE-Qwen, modernBERT) so models stay fast and affordable at petabyte scale

Prototype and launch hybrid dense/sparse retrieval pipelines

on vector DBs to build language-agnostic clustering and classification systems that power our intelligence layer

Own high-recall NER models

Build cross-dataset entity-resolution frameworks