Job Description
This job was selected by the SpringCube team to help AI, Data and Cloud Engineers discover relevant San Francisco Bay Area employers. Sign up to view the full employer details and apply directly with the hiring company.
Company Overview
A healthcare technology company focused on improving clinical documentation and enabling better understanding of medical conversations through artificial intelligence. Its enterprise-grade platform transforms patient-clinician conversations into structured clinical notes in real time and integrates with electronic medical record systems. The company develops auditable AI solutions designed to support clinicians and promote responsible deployment of generative AI across healthcare organizations.
Responsibilities
- Design, deploy, and maintain scalable Kubernetes clusters for AI model inference and training.
- Develop, optimize, and maintain machine learning model-serving infrastructure with a focus on high performance and low latency.
- Collaborate with ML and product engineering teams to scale backend infrastructure for AI-driven products, including model deployment, throughput optimization, and compute efficiency.
- Optimize compute-intensive workflows and improve GPU utilization for machine learning workloads.
- Build and maintain robust model API orchestration systems.
- Work with technical leadership to define and implement infrastructure scaling strategies that support long-term efficiency, reliability, and performance as the organization grows.
Qualifications
- 5+ years of experience building and deploying machine learning models in production environments.
- Deep understanding of container orchestration and distributed systems architecture.
- Expertise in Kubernetes administration, including Custom Resource Definitions (CRDs), operators, and cluster management.
- Experience developing APIs and managing distributed systems supporting both batch and real-time workloads.
- Strong communication skills and the ability to collaborate effectively across research and product engineering teams.
Preferred Qualifications
- Expertise with model-serving frameworks such as NVIDIA Triton Server, vLLM, and TensorRT-LLM.
- Experience with ML toolchains such as PyTorch, TensorFlow, or distributed training and inference libraries.
- Familiarity with GPU cluster management and CUDA optimization.
- Knowledge of infrastructure-as-code tools such as Terraform and Ansible.
- Experience with GitOps practices.
- Experience with container registries, image optimization, and multi-stage container builds for machine learning workloads.
- Experience orchestrating ASR or LLM models for Generative AI applications.
Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals globally.
- No Endorsement: SpringCube does not endorse or recommend any company or position listed.
- No Client Relationship: Listing a position does not mean SpringCube has a client, partnership, or employment relationship with the hiring company.
- To Apply: Use the Apply button to apply directly through the relevant hiring company’s application process.
- No Liability: SpringCube is not responsible for the hiring process, employment terms, recruitment decisions, or outcomes associated with any listed position.
