Staff Machine Learning Engineer

Cohere Health is a fast-growing clinical intelligence company that’s improving lives at scale by promoting the best patient-specific care options. As a Staff Machine Learning Engineer, you will lead technical initiatives on the Enterprise ML team, focusing on developing and deploying machine learning systems to enhance clinical workflows and decision-making processes.

Responsibilities:

• Design, build, and deploy advanced machine learning systems for retrieval, classification, prediction, and generative use cases
• Apply advanced statistical and ML techniques to extract insights from large-scale structured and unstructured healthcare datasets
• Lead model development across the ML lifecycle, including experimentation, training, evaluation, deployment, monitoring, and iteration
• Develop and oversee scalable, reusable codebases and ML infrastructure to support production use cases
• Collaborate cross-functionally with product managers, clinicians, data engineers, BI engineers, and design teams to translate business and clinical needs into robust ML solutions
• Drive experimentation by defining problem statements, forming falsifiable hypotheses, and designing rigorous evaluation frameworks tied to business outcomes
• Review, communicate, and present ML insights and results to technical and non-technical stakeholders, including executive leadership
• Serve as a technical mentor and advisor to junior engineers, providing guidance on ML best practices, experimentation, and system design
• Contribute as an expert advisor across multiple initiatives, helping shape ML strategy and performance tracking across the organization

Requirements:

• Master's degree (PhD preferred) in Computer Science, Data Science, Machine Learning, or a closely related quantitative field
• 8+ years of professional experience in applied machine learning or data science, including ownership of production ML systems
• Deep expertise in Python and modern deep learning frameworks (e.g., PyTorch)
• Hands-on experience building and deploying deep learning models (e.g., transformers) for NLP tasks
• Strong understanding of experimental design, model evaluation, and optimization for real-world production environments
• Experience leveraging cloud platforms (AWS preferred) across the ML lifecycle (training, deployment, monitoring)
• Proven ability to collaborate with product, business, and clinical partners to drive data-informed decision-making
• Excellent written and verbal communication skills, with experience presenting to both technical and non-technical audiences