Staff Machine Learning Engineer

<p><strong>The Future of Healthcare Needs You</strong></p> <p>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, <strong>Suki Assistant</strong>, uses cutting-edge AI to automate clinical documentation in real time, reducing the time it takes to complete notes by 72%. But that’s just the beginning.</p> <p>We’re now powering the next wave of voice-enabled healthcare experiences across telehealth platforms, care delivery tools, and specialty EMRs — through&nbsp;<strong>Suki Platform</strong>, our proprietary AI infrastructure.</p> <p>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, Javascript.</p> <p><strong>What will you do everyday?</strong></p> <p>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&nbsp; 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.</p> <p><strong>Requirements:*</strong></p> <ul> <li>7+ years in overall software experience, with at least 2+ years focused on shipping AI or machine learning systems to production</li> <li>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</li> <li>Strong working knowledge of LLMs, including prompting, orchestration, RAG, context and cost management</li> <li>Hands-on experience designing and implementing evaluation frameworks for LLM-based systems, including automated evals, human feedback loops, regression testing, and benchmark design</li> <li>Experience in building and maintaining dis