Senior Machine Learning Research Scientist
New
S
SmarterDxClinical AI, Healthcare
Remote (United States)Full-TimeSenior
Salary200,000 - 220,000 USD per year
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Job Details
- Required Skills
- PythonApache AirflowKubernetesPyTorchSnowflakeGitHubMLOps
Requirements
- Desire to translate research into tangible positive impact by deploying research into production engineering systems (MLOps)
- Ability and desire to communicate clearly and proactively when conveying or receiving scientific concepts, technical debugging or domain knowledge
- Deep "under-the-hood" understanding of modern neural network architectures and distributed training (e.g., 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
- 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)
Responsibilities
- Lead groundbreaking ML research and development at SmarterDx
- Collaborate with experienced engineers and clinicians to turn inventions into enterprise-grade products
- Establish a research agenda informed by emerging trends in AI
- Develop and rigorously evaluate proposed algorithms
- Deploy algorithms into production and monitor impact
- Collaborate with other machine learning research scientists to identify promising areas of AI/ML R&D
- Establish shared infrastructure to accelerate research efforts across the team
- Become a domain expert at clinical data and the healthcare ecosystem
- Own end to end model development including deployment into production and production monitoring (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
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