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Company Overview
A leading healthcare technology company is advancing precision medicine through AI-driven clinical solutions. Their proprietary platform integrates real-world evidence to provide actionable insights for physicians, enabling more informed treatment decisions and improved patient outcomes. The organization focuses on deploying advanced AI models at scale to enhance clinical workflows, accelerate research, and transform healthcare delivery.
Summary
The Staff/Senior Machine Learning Engineer will design, build, and maintain production AI pipelines and infrastructure for clinical applications, leveraging natural language processing and large language models (LLMs) at scale. This role will support clinical workflow optimization, trial matching, and medical research through innovative AI solutions.
Responsibilities
Build and operate production AI pipelines, including LLM-powered extraction, batch orchestration, and inference, ensuring reliability, cost-efficiency, and low latency
Design and maintain Airflow-based orchestration for batch clinical workflows
Implement observability systems (metrics, logging, alerting) to detect regressions proactively
Develop evaluation infrastructure to continuously measure clinical model output quality, including regression detection, drift management, dashboards, and gold-set management
Ship platform tooling and SDKs to accelerate ML Scientists and downstream users
Partner with ML Scientists to debug model output issues and identify root causes (data, prompt, pipeline)
Participate in on-call rotation for production systems
Collaborate with platform and infrastructure teams to optimize cloud services (GCP) for performance, security, and cost
Author and review design documents for cross-team projects
Raise the engineering bar through code and design reviews
Required Qualifications
Strong production experience with Python
Experience designing, building, and integrating microservices in production
Hands-on experience deploying data orchestration workflows (Airflow or equivalent)
Familiarity with cloud-native services (GCP preferred)
Built monitoring, observability, and alerting systems for production environments
Experience with at least one major ML framework (LangGraph, PyTorch, spaCy, or equivalents)
Strong written and verbal communication; experience authoring and reviewing RFCs, PRDs, or equivalent
Collaborative mindset with research scientists, PMs, and clinicians
Preferred Qualifications
Hands-on experience operating production systems, including on-call rotations and incident response
Experience building evaluation or quality measurement systems for ML or LLM outputs
Practical experience with production LLM applications (prompt engineering, agents, RAG, extraction pipelines, LLM evaluations)
Built internal platforms or SDKs supporting other engineers or scientists
Experience with clinical or biomedical data (EHR, genomics, pathology, clinical notes)
Contributions to relevant open-source projects
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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