Machine Learning Research Scientist - Distributed Post Training

Role

As a Machine Learning Research Scientist, you will lead groundbreaking ML research and development at SmarterDx, collaborating closely with experienced engineers and clinicians to turn your inventions into enterprise-grade products. With minimal supervision, you will establish a research agenda informed by emerging trends in AI, develop and rigorously evaluate your proposed algorithms, deploy your algorithms into production, and monitor impact. You will collaborate closely with other machine learning research scientists to identify the most promising areas of AI/ML R&D for SmarterDx and establish shared infrastructure to accelerate research efforts across the team.

What You'll Do

• Invent and deploy state-of-the-art machine learning algorithms
• Share insights and breakthroughs through publications in top-tier conferences, journals, blog posts, and other mediums to showcase your work
• Survey and share relevant AI/ML research and emerging technologies within the company to help others stay abreast of important trends
• Establish and own an ambitious research agenda with regular milestones
• Frequently present your work internally and solicit feedback from other machine learning research scientists, data scientists, engineers, and clinicians
• Provide constructive feedback to other ML research scientists on the team
• Collaborate cross-functionally with clinical, engineering, and product teams to understand key needs and prioritize research accordingly
• Work in a fast-paced and rewarding environment that demands high quality work with minimal supervision
• Collaborate cross-functionally with engineering to assist in configuring the correct infrastructure for training and inference
• Collaborate cross-functionally with engineering, data engineering, data analytics and data science to evaluate models in production, troubleshoot and update models based on real world results 

What You Bring

• Deep “under-the-hood” understanding of modern neural network architectures and distributed training. You can explain the differences between SwiGLU vs. sigmoid, GRUs vs. transformers vs SSMs, encoders vs. decoders, masked language models vs. autoregressive language models, tensor parallelism vs pipeline parallelism vs data parallelism, gloo vs mpi vs nccl, CUDA, Triton, etc.
• 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 create high-quality machine learning research, as demonstrated through first-author publications at top-tier conferences and journals (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR, AAAI, NEJM AI, JAMIA, npj Digital Medicine, arXiv)
• Understanding of recent and relevant AI/ML research trends and ability to quickly assess the quality of published ML research
• Ability and desire to translate research into production
• Ability to communicate clearly, as evidenced by blog posts, conference talks, published research papers, etc.

Nice To Haves

• Ph.D. (or Master’s degree with 3+ years experience) in Computer Science, Biomedical Informatics, Mathematics, Electrical Engineering, or a related field with an AI/ML focus

Our Tech Stack

• Python
• PyTorch
• Sagemaker
• Snowflake

Compensation

$200-220k base salary + equity