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SmarterDx · 1 day ago

Machine Learning Research Scientist

United States

Full-time

Remote

Senior Level

$200K/yr - $220K/yr

SmarterDx is a company that builds clinical AI to transform how hospitals translate care into payment. As a Machine Learning Research Scientist, you will lead ML research and development, collaborating with engineers and clinicians to create enterprise-grade products and establish a research agenda informed by AI trends.

Artificial Intelligence (AI)Health CareHospitalInformation Technology

Responsibilities

Hands-on implementing new methods and relevant baseline models (ML Research)

Working cross-functionally to deploy models into production (MLOps, MLE)

Data Science (data engineering, dataset curation, experimental design, model updates, product domain expertise)

Academics & Outreach (e.g., scientific reading & writing, publishing, presenting at conferences, recruiting)

Become a domain expert at clinical data and the healthcare ecosystem

Own end to end model development including deployment into production and production monitoring, learning Machine Learning Operations (MLOps)

Post training to align large language models (LLMs) on proprietary clinical data

Develop new self-supervised pre-training tasks for improving models

Develop novel retrieval, attribution and hallucination detection strategies for generative models

Develop novel methods for explaining and summarizing diagnostic classifications

Develop methods for selecting data sources to include in training (data-centric AI)

Develop novel graph-based algorithms for improving classification of diseases and procedures with few or no labels

Develop novel methods for multimodal data fusion (structured and unstructured data)

Long-sequence language modeling

Qualification

Machine Learning ResearchDeep Learning ModelsMLOpsNeural Network ArchitecturesData SciencePythonPyTorchScientific CommunicationCollaborationProblem Solving

Required

Desire to translate research into tangible positive impact by deploying research into production engineering systems (MLOps)

With scientific concepts, technical debugging or domain knowledge, ability and desire to communicate clearly and proactively when conveying or receiving

Deep “under-the-hood” understanding of modern neural network architectures and distributed training. ie knows the differences between SwiGLU vs. sigmoid, GRUs vs. transformers vs SSMs, encoders vs. decoders, masked language models vs. autoregressive language models, Megatron vs nanotron vs DeepSpeed

Extensive experience developing, implementing and training state-of-the-art deep learning models using multiple GPUs and nodes if necessary for large language models with frameworks such as PyTorch, JAX, etc

Ability to assess, understand and create high-quality machine learning research, as demonstrated through publications at top-tier conferences and journals (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR, AAAI, NEJM AI, JAMIA, npj Digital Medicine, arXiv)