Machine Learning Developer, Clover Health

• You have 3+ years of experience in Machine Learning Engineering or related roles in technology enabled companies, healthcare experience preferred but not required
• You have experience with Python, Python data science libraries (numpy, pandas, sklearn, tensorflow, pytorch, etc.), and deploying Python apps into production environments
• You have a solid foundation in feature engineering, feature selection, and AI techniques
• You have experience interpreting, modifying, and debugging the inputs and outputs of production ML models
• You have both built and refactored parts of distributed systems, especially ML systems
• You want to create impact with your work by finding ML-driven insights in the data to unlock value and improve health outcomes for real people
• You are comfortable acting autonomously on tasks in ambiguous and changing environments
• You value collaboration and feedback. You can communicate technically in clear terms— to your teammates and across the technology team more broadly
• You are willing and able to help your teammates grow by providing (and receiving) respectful and constructive feedback, and disclosing your unique insights with everyone
• You enjoy working in a fluid environment, defined by priorities that adapt to our larger goals. You can bring clarity to ambiguity on projects while remaining open-minded to new information that might change your mind
• You are not hesitant to jump in to help fix things that are broken and you are encouraged to make sustainable systems. You are happy to fill in the gaps to reach a goal where necessary, even if it does not always fit your job description
• You have a genuine interest in what good technology can do to help people and have a positive attitude about tackling hard problems in an important industry

• We’re looking for a Machine Learning Engineer II to help us build a revolutionary new health care business
• Counterpart Health uses Machine Learning (including NLP/LLMs) to leverage our data to help keep patients healthy and out of the hospital by getting them targeted care
• By predicting avoidable adverse events, our ML infrastructure is central to working on our central mission, and has a direct impact on improving clinical care
• You will help build systems and tools that support the data needs of a diverse organization and contribute to the expansion of the ML capabilities of our Platform
• Create, debug, interpret and improve production ML models
• Design, implement and validate pieces of our high-reliability, distributed platforms for ML
• Build the tools and validation processes that help Counterpart translate insights into action at scale
• Use existing commercial and open source tools to create a robust production platform
• Work closely with Counterpart’s Data Science and Engineering teams to ensure that the ML platform is providing real value
• Document, iterate, and provide tutorials to ensure Data Scientists and Engineers are able to use your tools easily
• Success in this role looks like:
• Within 90 days: Successfully able to deliver a well-scoped project that improves machine learning models or systems
• Within 6 months: Able to increasingly own larger pieces of the system and work more autonomously in places you have experience
• In the future: Growing in ownership to increasingly large and complex pieces of our ML models and systems

• Paid parental leave and a life insurance policy
• Commuter benefit program
• 2 paid volunteer days for an organization of your choice
• 401(k) contributions
• Home office reimbursement for full-time employees
• 18 paid vacation days, 9 paid sick days, 10 company holidays, and a floating holiday
• Medical, dental, and vision plans; healthcare and dependent care FSA as well as a health and wellness benefit

Total funding: $925m

Clover Health is a medical insurance company operating as a Preferred Provider Orgaization for Medicare Advantage, the Medicare plan for elderly American citizens. It aims to reduce the doctor-insurer friction to increase visibility into the health of patients.