Principal ML/AI Engineer

Company: The mission of Well (https://www.well.co/) is to transform healthcare through our unique impact to our members' health and happiness. We do this through our differentiated consumer experience and world-class data and analytics engine that drive engagement and behavior change. Our product -- a consumer health engagement platform -- integrates concierge services, behavioral health, telemedicine, care management and wellness services to drive sustained engagement, lower costs and improve the health of members. In addition to our product, we know our team makes us unique. We’re a highly diverse and engaged organization whose employees are passionate about the mission of the company and whose management is passionate about the employees.

Position Title: Principal ML/AI Engineer 

Reporting To: Senior Director of Data Science and Machine Learning

Location: New York, NY

Compensation Information: $160,000-$180,000 depending on qualifications

Position Summary:

The overall goal is to engage our members to drive better health outcomes. Our modeling and optimization platform (the Health Engine) contributes directly to our product through machine learning, statistics, analysis, and computation. The next member of our team will drive ML and AI projects from beginning to end: frame business questions, collect and analyze data, research, prototype models, build/deploy pipelines, and share insights. They will work collaboratively within a team aiming to make a positive impact on the organization and on our members’ overall health.

Key Responsibilities:

• Identify opportunities to use and tune generative models using current and emerging methodologies in machine learning and AI. Lead improvements to our recommendation engines, natural language processing/understanding, develop AI tools to help teams improve operations, and enhance our data-driven decision making.
• Leverage and utilize a wide range of disparate data sources across our ecosystem and within healthcare (app, claims, EHR, wearables).
• Architect and develop our data and machine learning ecosystem, working to run experiments and deploy models to production environments and that operate effectively at scale.
• Provide generative modeling expertise within the team and across the organization.
• Take personal responsibility for keeping all Well systems and data, including sensitive member data, secure and safe, according to Well data and security policies and HIPAA guidelines

Preferred Qualifications:

• 7+ years of work/industry experience in data science or machine learning roles
• Advanced degree (MS or PhD) in a quantitative field. Strong grasp and theoretical understanding of machine learning
• Demonstrated experience using Python for machine learning and AI (pytorch, huggingface, llamaindex, axolotl, peft, trl, sentencetransformers, sklearn, numpy, pandas)
• Experience with LLMs, RAGs, and generative models (applying, prompting, fine tuning)
• Proficient with SQL and databases
• Expertise using Unix/OSX from the command line, version control (Git), and general software development best practices for contributing to a collaborative code base 
• Experience executing analyses in the cloud (GCP, AWS)
• Strong communication and collaboration skills required. Ability to communicate technical modeling concepts and relevant aspects of modeling platforms to non-technical audiences

Additional Job Information

Well is on a mission to redefine the healthcare experience. This is an opportunity to re-shape healthcare for America. We are developing solutions to improve the quality and affordability of healthcare.  We welcome team members who are passionate about that mission. We embrace diversity and are committed to building an inclusive team.

Well is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.  We seek diversity and encourage individuals from underrepresented groups to apply.