Staff AI Researcher

Aledade United States

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Posted 19 Jul 2026

Last seen 19 Jul 2026

Location United States

Lifecycle aged

Grade F

Why this grade This listing scored 18/100, which is an F. It lost the most ground on pay transparency. See the breakdown

Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
Corroboration 5 / 10 Whether more than one source carries this listing.
Role specificity 0 / 10 Whether the listing is tagged well enough to tell what the role actually is.
Freshness 0 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
Pay transparency 0 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.

-15 Ghost-job penalty — Deducted for signals that this posting may not be a real, currently-open role — staleness, repeated relisting, or talent-pool language.

Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →

As a Staff AI Researcher, you will develop AI solutions that will improve health for millions of people. Here at Aledade we empower primary care physicians with technology to keep their patients healthy and prevent unnecessary hospitalizations. You will partner with other engineering and analytics teams, bringing AI technology into existing products and workflows. As a Staff AI Researcher, you will lead the way to harness knowledge from one of the most extensive data sets of medical records, diagnoses, claims, and prescriptions. You will have a unique opportunity to train, fine-tune and use AI models using medical data we collect from millions of patients across the country.

Primary Duties:

Build working prototypes using off-the-shelf and novel AI techniques to deliver higher optimization levels for the company.
Work with large, complex data sets. Solve difficult, non-routine analysis problems to harvest data.
Re-design current pipelines and systems to meet the growing data and query needs.
Implement techniques for fine-tuning and adapting pre-trained generative models to specific healthcare domains or tasks.
Develop evaluation metrics and benchmarks to assess the quality and performance of AI/ML models.
Experience in designing and implementing feature engineering pipelines, including data processing, feature extraction, and transformation to optimize model performance.
Set and uphold the standard for engineering processes to support high-quality engineering, including style and code checking, test harnesses, and release packaging.
Deliver working POC solutions solving speed, scalability and time-to-market tradeoffs.

Minimum Qualifications:

BS/BTech (or higher) in Computer Science or a related field required.
3+ years of relevant deep learning and LLM work experience.
8+ years of relevant machine learning and statistical analysis experience.
3+ years or Python language experience.
Experience in addressing challenges from incomplete, unrepresentative, and mislabeled data.
Experience working with large-scale distributed systems at scale and statistical software (e.g. Spark).
3+ years of demonstrated proficiency in selecting the right tools given a data optimization problem.

Preferred KSA’s:

Ph.D. or Master's degree in a quantitative discipline (e.g., Computer Science[with AI/ML Major], Statistics, Operations Research, Economics, Mathematics, Physics) or equivalent practical experience.
Proficiency in communicating analysis and establishing confidence among audiences who do not share your disciplinary background or training.
Experience with security and systems that handle sensitive data.
Experience with Databricks/MLflow.
Experience with designing and implementing production-ready agentic systems.
Proficiency in at least one major deep learning framework (e.g. PyTorch, Tensorflow, Keras, etc), with the ability to design and implement deep learning architectures.
Demonstrated leadership and self-direction.
First-author publications at peer-reviewed conferences (e.g. NeurIPS, ICML, ACL, JSM, KDD, EMNLP).
Winners in ACM-ICPC, NOI/IOI, Kaggle.
Working knowledge of health-tech systems, like Electronic Health Records, Clinical data, etc.

Physical Requirements:

Sitting for prolonged periods of time. Extensive use of computers and keyboard. Occasional walking and lifting may be required.

Originally posted on Himalayas

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19 Jul 2026 Himalayas first sighting

Seen on 1 board over 0 days.