About the Role
As a Staff Machine Learning Research Scientist at SmarterDx, you will set technical direction for cutting-edge ML research and translate it into real-world clinical impact. You’ll work at the intersection of research, engineering, and healthcare, partnering with engineers and clinicians to build systems that deeply understand patient records and improve hospital outcomes. This is a senior, high-impact role where you’ll not only execute on ambitious ideas but also shape the team’s research agenda and standards.
You will be expected to operate with a high degree of autonomy—identifying promising research directions, critically evaluating academic work, and ensuring that what gets built is both scientifically sound and practically useful. Your work will directly influence how we evaluate models, detect hallucinations, and build high-quality datasets, ultimately improving the reliability of AI in healthcare.
What You’ll Do
- Lead end-to-end ML research, from idea generation to production deployment and monitoring
- Design, implement, and evaluate novel methods for LLM alignment on proprietary clinical data
- Develop and rigorously evaluate approaches for hallucination detection, attribution, and model reliability
- Build and curate high-quality datasets, with a strong emphasis on evaluation design and benchmark integrity
- Critically assess academic literature to identify strong vs weak methods, and translate the best ideas into practice
- Establish best practices for experimental design, including statistically sound evaluation and reproducibility
- Collaborate cross-functionally with engineering to productionize models (MLOps, infra, deployment)
- Develop methods for long-context and multimodal modeling (structured + unstructured clinical data)
- Mentor other researchers and help raise the bar for research quality across the team
- Contribute to external presence through papers, talks, and recruiting
What You Bring
- Strong track record of ML research, ideally with publications in top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, AAAI, etc.)
- Proven ability to distinguish high-quality vs low-quality research, especially in fast-moving areas like LLMs
- Deep understanding of LLM failure modes, particularly hallucinations, and how to evaluate and mitigate them
- Experience designing rigorous evaluation frameworks and building high-quality test datasets
- Strong intuition for dataset quality, bias, and benchmark design (data-centric AI mindset)
- Hands-on experience training large-scale deep learning models (multi-GPU / distributed systems)
- Deep understanding of modern neural architectures (transformers, SSMs, encoder/decoder models, etc.)
- Strong programming skills in Python and ML frameworks such as PyTorch or JAX
- Experience deploying ML models into production systems and monitoring their performance
- Clear and proactive communicator, able to explain complex ideas and critique work effectively
Nice To Haves
- Experience with inference optimization techniques (e.g., vLLM, KV caching, speculative decoding)
- Familiarity with MLSys concepts (parallelism strategies, distributed training infrastructure)
- Experience working with clinical or healthcare data
- Background in retrieval systems, graph-based learning, or multimodal modeling
Our Tech Stack
- PyTorch, Hugging Face Transformers, Python
- AWS (MWAA), Kubernetes, SLURM
- DeepSpeed, TorchTune
- Snowflake, Airflow, GitHub