Job Description

Clover is reinventing healthcare by working to keep people healthier.

We value diversity — in backgrounds and in experiences. Healthcare is a universal concern, and we need people from all backgrounds and swaths of life to help build the future of healthcare. Clover’s engineering team is empathetic, caring, and supportive. We are deliberate and self-reflective about the team and culture that we are building, looking for engineers that are not only strong in their own aptitudes but care deeply about aiding in each other’s growth.

We’re looking for a Senior Machine Learning Engineer to help us build a revolutionary new health care business. Clover employs Machine Learning to use our data to help keep beneficiaries healthy and out of the hospital by getting them targeted care. By predicting avoidable adverse events, our machine learning infrastructure is central to working on our central mission, and has a direct impact on our beneficiaries. You will help build systems and tools that augment the data needs of a diverse organization and contribute to the expansion of the Machine Learning capabilities of our Data Platform.

As a Senior Machine Learning Engineer, you will:

• Create, debug, interpret and improve production machine learning models.
• Design, implement and validate high-reliability, distributed platforms for machine learning.
• Build the tools and validation processes that help Clover translate insights into action at scale.
• Use existing commercial and open source tools to create a robust production platform.
• Work closely with Clover’s Data Science and Engineering teams to ensure that the Machine Learning Platform is providing real value.
• Document, iterate, and provide tutorials to ensure Data Scientists are able to use your tools easily.

You will love this job if:

• You want to create impact with your work by finding machine learning-driven insights in the data to unlock value and improve health outcomes for real people.
• You are comfortable acting autonomously in ambiguous and changing environments.
• You value collaboration and feedback. You can communicate technical vision in clear terms— to your teammates and across the technology team more broadly. You are willing and able to help your teammates grow by demonstrating best practices, providing (and receiving) respectful and constructive feedback, and disclosing your unique insights with everyone.
• You enjoy working in a fluid environment, defining priorities that adapt to our larger goals. You can bring clarity to ambiguity 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.

You should get in touch if:

• You have 5+ years of experience in Machine Learning Engineering 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 machine learning techniques.
• You have experience interpreting, modifying, and debugging the inputs and outputs of production machine learning models.
• You have both built and refactored complex distributed systems, especially machine learning systems.
• You have scaled the impact of other engineers and data scientists through mentorship, development of reusable libraries, and documentation.

#LI-REMOTE

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. We are an E-Verify company.

Job Categories: Equal Opportunities. Job Types: Full-Time. Salaries: Not Disclosed.

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