Staff Machine Learning Engineer

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.
• 7+ 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.
• Experience with generative AI, large language models, agentic systems, or Retrieval Augmented Generation (RAG).
• Experience driving automation in healthcare or regulated environments.
• Familiarity with healthcare workflows such as claims, coding, utilization management, or network operations.
• Experience working with unstructured healthcare data (e.g., clinical notes, OCR, document understanding).
• Hands-on experience with AWS tools such as SageMaker Studio.
• Interview Process*:
• Connect with Talent Acquisition
• Meet with the Hiring Manager
• Behavioral Interview(s)
• Case Study
• Interview with Senior Leadership
• *Subject to change
• About Cohere Health:
• Cohere Health's innovations continue to receive industry wide recognition. We've been named to the 2025 Inc. 5000 list and in the Gartner® Hype Cycle™ for U.S. Healthcare Payers (2022-2025), and ranked as a To

Benefits

Additional Information

Opportunity Overview: As a Staff Machine Learning Engineer, you will play a critical technical leadership role on Cohere Health's Enterprise ML team, with a primary focus on powering and scaling machine learning capabilities within our Intake product. You will apply state-of-the-art machine learning, large language models, and agentic architectures to complex clinical and operational intake workflows, helping automate decision-making, improve data quality, and reduce administrative burden for clinical teams. In this role, you will partner closely with clinical operations, product, and engineering teams to uncover hidden drivers, inform strategic decisions, and deploy production-grade ML systems that directly impact how members and providers experience Cohere Health at the front door of care. This role blends deep hands-on technical work with strategic influence, mentorship, and ownership across multiple work streams, while contributing to broader Enterprise ML initiatives.

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