Machine Learning Engineer III (nlp)

Suki AI Suki AI · Vertical AI · Suki, India · Engineering

Machine Learning Engineer III (NLP) at Suki AI, a healthcare technology company using generative AI for clinical documentation. The role focuses on enhancing NLP capabilities, managing datasets, optimizing ML infrastructure, and deploying state-of-the-art deep neural network models, including LLMs, RAG, and evaluation frameworks. The goal is to reduce administrative burdens for clinicians.

What you'd actually do

  1. Leverage your NLP expertise to enhance our computational infrastructure and improve our technical approaches and algorithms.
  2. Working directly on the platform powering our domain services, you'll collaborate with cross-functional teams to address NLP challenges and scale our platform for thousands of doctors.
  3. You'll manage and curate large datasets, ensuring data quality and relevance for training and evaluation.
  4. You'll monitor and analyze system performance metrics, identifying areas for optimization and implementing necessary improvements.
  5. By staying updated on NLP and Generative AI advancements, you'll incorporate innovations to maintain our competitive edge, reducing doctors’ administrative burdens and significantly impacting their daily lives.

Skills

Required

  • 5+ years in overall Machine Learning experience
  • at least three years focused on NLP and Contextual Embeddings
  • moving at least one NLP model to an enterprise production system
  • deployment of state-of-the-art deep neural network models
  • building and maintaining distributed backend services for ML/AI Applications
  • Strong working knowledge of LLMs, including prompting, orchestration, RAG, context and cost management
  • designing and implementing evaluation frameworks for LLM-based systems

Nice to have

  • Strong grasp of CS fundamentals including abstractions, algorithms, probability, data structures and system design

What the JD emphasized

  • Proven record of an end to end workflow and with moving at least one NLP model to an enterprise production system.
  • Expert in the understanding, practice, and deployment of state-of-the-art deep neural network models.
  • Strong working knowledge of LLMs, including prompting, orchestration, RAG, context and cost management
  • Hands-on experience designing and implementing evaluation frameworks for LLM-based systems, including automated evals, human feedback loops, regression testing, and benchmark design

Other signals

  • ambient listening
  • clinical documentation
  • generative AI
  • NLP
  • LLMs