Job Overview
Location
Remote - USA
Job Type
Full-time
Category
Machine Learning Engineer
Date Posted
March 19, 2026
Full Job Description
đź“‹ Description
- • As a Senior Machine Learning Engineer at Clover Health Investments, Corp., you will play a pivotal role in advancing Counterpart Health’s mission to transform primary care through AI-driven tools like Counterpart Assistant, directly improving patient outcomes and reducing healthcare costs by enabling earlier diagnosis and proactive chronic condition management.
- • You will design, implement, and validate high-reliability, distributed platforms for machine learning, natural language processing (NLP), and large language models (LLMs), ensuring scalable and robust systems that translate data insights into clinical action at scale.
- • Your day-to-day responsibilities include creating, debugging, interpreting, and improving production ML/NLP/LLM models; building tools and validation processes that operationalize insights; leveraging commercial and open-source tools to construct production-grade platforms; collaborating closely with Data Science and Engineering teams to align platform capabilities with business value; and documenting, iterating, and providing tutorials to empower data scientists with easy-to-use tools and best practices.
- • You will thrive in a fluid, remote-first environment where autonomy, collaboration, and constructive feedback are valued—where you define priorities amid ambiguity, mentor peers through reusable libraries and knowledge sharing, and proactively address gaps to sustainably solve complex problems in healthcare technology.
- • This role offers the opportunity to make tangible impact by applying ML/NLP/LLM expertise to real-world health challenges, deepen your expertise in production ML systems and LLM infrastructure, grow as a technical leader through mentorship and system design, and contribute to a mission-driven organization committed to equity, innovation, and improving lives through technology.
🎯 Requirements
- • 5+ years of experience in Machine Learning Engineering roles within technology-enabled companies, with healthcare experience preferred but not required.
- • Proficiency in Python and core data science libraries (numpy, pandas, scikit-learn, TensorFlow, PyTorch, etc.), including deploying Python applications into production environments.
- • Hands-on experience with Natural Language Processing (NLP) and/or Large Language Models (LLMs), including feature engineering, model interpretation, debugging inputs/outputs, and scaling impact through reusable tools and mentorship.
🏖️ Benefits
- • Competitive base salary ($150,000–$200,000 USD), equity opportunities, performance-based bonuses, 401k matching, and regular compensation reviews to reward exceptional contributions.
- • Comprehensive medical, dental, and vision coverage, plus mental well-being initiatives including No-Meeting Fridays, monthly company holidays, access to mental health resources, and a generous flexible time-off policy.
- • Professional development support via learning programs, mentorship, funding for growth, and regular performance feedback; plus perks like Employee Stock Purchase Plan (ESPP), office setup reimbursement, monthly cell phone/internet stipend, remote-first culture, and paid parental leave.
Skills & Technologies
About Clover Health Investments, Corp.
Clover Health Investments, Corp. is a healthcare technology company focused on improving the health and well-being of its members. The company aims to make healthcare affordable and accessible for everyone, particularly seniors and those in underserved communities. Clover Health integrates insurance and healthcare services, offering a range of Medicare Advantage plans. Their approach emphasizes proactive care, leveraging data analytics and a dedicated care team to help members navigate the healthcare system, manage chronic conditions, and achieve better health outcomes. They strive to simplify the healthcare experience through technology and personalized support, making quality care more attainable and understandable for their members.
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