Machine Learning Engineer

Overview

The Future of Healthcare Needs You. At Suki, we’re building technology that listens, understands, and gets out of the way — so clinicians can get back to being clinicians. Our flagship product, Suki Assistant, uses cutting-edge AI to automate clinical documentation in real time, reducing the time it takes to complete notes by 72%. We’re now powering the next wave of voice-enabled healthcare experiences across telehealth platforms, care delivery tools, and specialty EMRs — through Suki Platform, our proprietary AI infrastructure.

Clinicians using Suki spend over 70% less time on admin. Our customers see a 9X ROI in year one. And we’re just getting started. Suki is reimagining the healthcare technology stack to make the lives of clinicians better. Our tech stack includes GCP, Kubernetes, Vertex AI, Golang, Python, C++, PyTorch, JAX, Transformers, GPT, LLAMA, MoE, gRPC, Java script.

The Machine Learning team forms the core of Suki's product, driving innovation and predictive excellence. You ll leverage your NLP expertise to enhance our computational infrastructure and improve our technical approaches and algorithms. 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. You ll manage and curate large datasets, ensuring data quality and relevance for training and evaluation.

You ll monitor and analyze system performance metrics, identifying areas for optimization and implementing necessary improvements. 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.

• 7+ years in overall software experience, with at least 2+ years focused on shipping AI or machine learning systems to production
• Demonstrated end-to-end ownership of delivering at least one AI system to enterprise production, spanning the full workflow from problem framing, data pipelines, and model or agent development, through system integration, deployment, evaluation, monitoring, and continuous iteration
• 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
• Experience in building and maintaining distributed backend services for ML/AI applications
• Strong grasp of CS fundamentals including abstractions, algorithms, probability, data structures and system design
• We don’t necessarily expect to find a candidate that has done everything listed, but you should be able to make a credible case that you’ve done most of it and are ready for the challenge of adding new skills to your resume.

• Expertise:
  You know how to train models. You understand which models work best, and use LLMs only when necessary. You watch out for recall, accuracy, and precision.
• Action oriented:
  You love to build, ship fast, iterate, and delight users.
• Creativity:
  You enjoy listening to user feedback and building products in novel ways, resourceful and adaptable.
• Problem solving:
  You use data to guide decisions, optimize relentlessly, and understand the impact on the business.
• Humility:
  You are collaborative and work well in a team without ego.
• Adaptability:
  You thrive in a fast-moving organization that uses lightweight processes and cutting-edge technology.
• Confidence:
  You trust your abilities and aspire to grow.
• Communication:
  You are a clear communicator and consensus-builder.

• A mission that matters:
  Suki is making healthcare technology invisible and assistive—so clinicians can focus fully on patient care.
• On a roll:
  Named by Fast Company as one of the most innovative companies;
  Google’s Partner of the Year for AI/ML;
  Forbes 50 AI companies.
• An AI product that delivers:
  Our AI is used by real clinicians at real health systems — and it works.
• A team that gets it:
  We’re former Googlers, Apple…