ML Engineer: Speech & LLMs

About Knowtex

Knowtex is building the future of voice AI operating systems for clinicians, transforming how healthcare documentation happens at the point of care. We are experiencing rapid growth across both commercial health systems and federal healthcare, with our ambient documentation platform scaling to thousands of clinicians across hundreds of specialties.

We are at an inflection point where advances in speech, language models, and clinical AI can fundamentally change how clinicians interact with technology, giving them more time to focus on what matters most: their patients.

Position Overview

We are hiring two ML Engineers / Researchers to help build the next generation of Knowtex's AI stack.

We are looking for researchers with deep expertise in one of two areas:

Speech & Audio: Build state-of-the-art medical speech-to-text systems using our large proprietary dataset of real-world clinical audio, with the goal of bringing more of our speech stack in-house.

Large Language Models: Develop and optimize models for clinical documentation and structured clinical reasoning, improving quality, cost, latency, and control.

You do not need to be an expert in both areas. We are looking for exceptional depth in either speech/audio modeling or LLMs.

These are research-heavy roles with a direct path to production. You will design experiments, build datasets and evaluation systems, train and fine-tune models, and work closely with engineering and clinical teams to deploy successful approaches at scale.

This role plays a central part in defining Knowtex's long-term ML strategy.

Key Responsibilities

Speech & Audio

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

Research approaches for reducing inference cost and latency while maintaining or improving clinical quality

Across Both Tracks

Move quickly from idea → dataset → experiment → evaluation → production

Design experiments that clearly measure whether an approach improves real-world clinical outcomes

Build datasets, benchmarks, and evaluation infrastructure that make model improvements measurable and reproducible

Collaborate closely with clinicians, applied ML engineers, and platform engineers

Take successful research beyond prototypes and help deploy models into production

Balance model quality with latency, inference cost, reliability, and scalability

Required Qualifications

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 Qualifications

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

Experience with supervised fine-tuning, distillation, preference optimization, or reinforcement learning

Experience building LLM evaluation systems and model benchmarks

Experience serving and optimizing open-weight models at scale

Experience with structured generation, tool use, or agentic systems

For Either Track

Experience in healthcare AI, clinical NLP, or medical speech

Familiarity with clinical documentation workflows and medical terminology

Knowledge of coding systems such as ICD-10, CPT, E&M, or SNOMED

Publications at leading ML, NLP, or speech conferences

Experience deploying ML systems in HIPAA-compliant or regulated environments

Experience working in fast-moving startup environments where researchers own projects from experimentation through production

Technical Environment

AWS

Python, PyTorch

Transformer-based LLM and speech architectures

Open-weight and frontier language models

Large-scale clinical audio and text datasets

Distributed model training and inference

GPU-based model serving and optimization

Real-time speech and clinical AI pipelines

Structured clinical evaluation and benchmarking infrastructure

Compensation & Benefits

Competitive salary

Meaningful equity compensation

Unlimited PTO

Premium health, dental, and vision coverage

401(k) plan

Work model: Hybrid In-person