Knowtex ML Engineer: Speech & LLMs at Knowtex develops state-of-the-art speech recognition models or large language models for clinical documentation and reasoning. The role involves research, model training, evaluation, and deployment at scale.
Responsibilities
Develop and train speech recognition models optimized for medical conversations across hundreds of specialties
Leverage Knowtex's large proprietary clinical audio dataset to train and fine-tune domain-specific speech models
Research approaches for improving medical terminology recognition, speaker attribution, punctuation, timestamps, and robustness across accents and clinical environments
Build rigorous speech evaluation frameworks beyond traditional WER, including medical terminology and clinically significant error measurement
Explore modern speech architectures, self-supervised learning, speech foundation models, and audio-language models
Optimize models for low-latency, real-time inference at production scale
Large Language Models
Develop and optimize models for generating high-quality clinical documentation, including SOAP notes and specialty-specific note formats
Build models for downstream clinical tasks such as medication extraction, orders, ICD-10 coding, E&M coding, patient visit summaries, and other structured clinical artifacts
Evaluate open-weight and proprietary model architectures and determine where fine-tuning, distillation, structured generation, or task-specific models can outperform general-purpose API-based approaches
Fine-tune and post-train models using Knowtex's proprietary clinical datasets
Develop rigorous evaluation frameworks for clinical accuracy, hallucinations, completeness, formatting, and clinician preferences
Qualification
Strong expertise in Python and PyTorchExperience trainingBachelor’sFor Speech ResearchersFamiliarity with speaker diarizationFor LLM Researchers
Required
2+ years of experience in machine learning research or ML engineering, with deep expertise in speech/audio modeling or large language models
Strong expertise in Python and PyTorch
Deep understanding of modern transformer architectures and model training techniques
Experience training, fine-tuning, or post-training large neural models
Strong experimental methodology and ability to independently design and execute research projects
Experience working with large-scale datasets and distributed training environments
Ability to translate research results into production systems
Strong understanding of model evaluation and benchmarking
Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, or a related technical field, or equivalent research experience
Preferred
For Speech Researchers
Deep experience with automatic speech recognition (ASR)
Experience training or fine-tuning Whisper, Conformer, wav2vec, or similar speech architectures
Experience with large-scale audio datasets and speech data pipelines
Familiarity with speaker diarization, voice activity detection, streaming ASR, or audio-language models
Experience optimizing speech models for real-time inference
For LLM Researchers
Experience fine-tuning or post-training open-weight LLMs
