Applied Machine Learning Engineer (all levels)

Role Description

Your North Star: As our first machine learning hire, you will be at the forefront of training and deploying ML systems, directly empowering our clinical team to provide the best care to our patients.

Examples of how you will contribute your expertise:

• Create predictive models to identify patients most likely to benefit from proactive outreach.
• Develop models to detect and categorize urgent or important patient messages, improving response times.
• Build risk prediction models to identify potential health complications early, guiding personalized care.
• Implement retrieval augmented generation (RAG) to query and extract insights from FHIR records and patient charts.

Who you are

You have a passion for using machine learning to affect real, tangible good in the world. You are also excited to play a big role bringing machine learning into our products and operations. As a versatile ML practitioner, you're comfortable wearing multiple hats and adapting to diverse challenges across the ML lifecycle.

Specifically, you have:

• At least two years of end-to-end experience of training, evaluating, testing and deploying machine learning in production environments
• Experience building data pipelines leveraging data from many sources as well as designing database schemas.
• Proven ability to extract and articulate valuable insights when data is difficult to model, e.g. sparse features or uncertain outcomes.
• Strong communication skills with the ability to explain complex ML concepts to both technical and non-technical audiences.

Bonus points if you have any of the following

• Have an advanced degree in which you applied statistical methods, such as Bayesian approaches like MCMC, to understand complex problems.
• Bring hands-on experience with cloud-based ML platforms, particularly on Google Cloud.
• Have experience applying machine learning in healthcare settings or working with medical data.
• Have published research or contributed to open-source ML projects

Why you should join our team

By joining Pomelo, you will get in on the ground floor of a fast-moving, well-funded, and mission-driven startup where you will have a profound impact on the patients we serve. And you'll learn, grow, be challenged, and have fun with your team while doing it.

We strive to create an environment where employees from all backgrounds are respected. We value working across disciplines, moving fast, data-driven decision making, learning, and always putting the patient first. We also offer:

• Competitive healthcare benefits
• Generous equity compensation
• Unlimited vacation
• Membership in the First Round Network (a curated and confidential community with events, guides, thousands of Q&A questions, and opportunities for 1-1 mentorship)

At Pomelo, we are committed to hiring the best team to improve outcomes for all mothers and babies, regardless of their background. We need diverse perspectives to reflect the diversity of problems we face and the population we serve. We look to hire people from a variety of backgrounds, including but not limited to race, age, sexual orientation, gender identity and expression, national origin, religion, disability, and veteran status.

Our salary ranges are based on paying competitively for our company’s size and industry, and are one part of the total compensation package that also includes equity, benefits, and other opportunities at Pomelo Care. In accordance with New York City, Colorado, California, and other applicable laws, Pomelo Care is required to provide a reasonable estimate of the compensation range for this role. Individual pay decisions are ultimately based on a number of factors, including qualifications for the role, experience level, skillset, geography, and balancing internal equity. A reasonable estimate of the current salary range is $135,000 - $230,000. We expect most candidates to fall in the middle of the range. We also believe that your personal needs and preferences should be taken into consideration, so we allow some choice between equity and cash.