As a Staff AI Researcher, you will design and implement machine learning and artificial intelligence solutions aimed at enhancing the health of millions. At Aledade, we support primary care doctors with advanced technology to help them maintain their patients’ health and reduce avoidable hospital visits. You will collaborate with engineering and analytics teams to integrate AI into current products and processes.

In this role, you will take the lead in leveraging one of the largest collections of medical data—including patient records, diagnoses, insurance claims, and prescriptions. You’ll have the rare opportunity to train, refine, and apply AI models using data from millions of patients nationwide.

Primary Responsibilities:

– Train and optimize models using both standard and innovative ML/AI methods to address key business challenges.
– Work with large and complex datasets, performing advanced and non-standard analyses to extract insights.
– Develop functional proof-of-concept solutions that balance performance, scalability, and speed to deployment.

Minimum Qualifications:

– Bachelor’s degree in Statistics, Data Science, Computer Science, or a related field.
– At least 8 years of experience in statistical analysis.
– At least 8 years of experience in machine learning, including model development, hyperparameter tuning, feature engineering, and validation.
– Background in Epidemiology, especially related to chronic disease modeling.
– 5–7 years of experience with selecting, deploying, and optimizing ML tools and frameworks for large-scale applications.
– Minimum of 3 years of experience with Python.
– At least 2 years of experience in deep learning and large language models.
– At least 2 years working with distributed systems and statistical tools like Spark.
– Experience managing challenges related to incomplete, biased, or mislabeled data.
– Proven record of impactful contributions such as publications, patents, or successful large-scale AI implementations.

Preferred Knowledge, Skills, and Abilities:

– Ph.D. or Master’s degree in a quantitative field such as Computer Science (with AI/ML focus), Statistics, Operations Research, Economics, Mathematics, or Physics, or equivalent hands-on experience.
– Familiarity with public health, particularly value-based care and risk adjustment.
– Understanding of healthcare technologies, including electronic health records and clinical data systems.
– Ability to clearly communicate complex analyses to audiences without technical backgrounds.
– Experience with secure systems and handling sensitive data.
– Proficiency with statistical tools such as R, SAS, or Python’s statistical libraries.
– Demonstrated leadership and initiative.
– First-author publications in top-tier conferences (e.g., NeurIPS, ICML, ACL, JSM, KDD, EMNLP).
– Recognition in data competitions like the ACIC Data Challenge or Kaggle.

Physical Requirements:

– Ability to sit for extended periods and work extensively on a computer. Occasional walking and lifting may be required.