ML Engineer: Speech & LLMs

1 нед. назад

200k–300k USD / yearUSAHybrid

speech-to-textlarge language modelsmachine learningdata evaluationmodel trainingfine-tuning

Build and optimize AI models focusing on medical speech-to-text and clinical large language models for healthcare documentation.

Требования

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

Условия

Competitive salary
Meaningful equity compensation
Unlimited PTO
Premium health, dental, and vision coverage
401(k) plan
Work model: Hybrid In-person

Другое

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.
We are hiring two ML Engineers / Researchers to help build the next generation of '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 's long-term ML strategy.
Develop and train speech recognition models optimized for medical conversations across hundreds of specialties
Leverage '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
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 '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
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
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
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
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
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