Engineering Internship

Tandem•New York, NY

1d•Onsite

About The Position

We are looking for full-stack and platform engineering interns who want to work on real production use cases of LLMs and help us scale to consumer and physician demand. You will take on a specific business problem and lead the prototyping, evaluation, and product integration of AI-powered solutions. Your responsibility will include exploring both established and emerging methods and determining what gets us to the best outcome. You will be expected to drive your project independently, but you will work closely with individuals on our Engineering, Data, GTM, and Operations teams to work through key decisions, get feedback, and launch your features. Summer internships are full-time at our office in NYC. In addition to working as part of our Engineering team, you will have the option to spend a portion of your time shadowing or working on a GTM-focused project with our GTM team or CEO. We are flexible on start date, with potential to extend beyond the Summer into the Fall semester.

Requirements

Strong programming skills and general Computer Science knowledge
Product engineering experience such as building web apps full-stack, integrating with relevant APIs and services, figuring out what to build and then iterating
Track record of moving quickly, finding shortcuts, and going to unreasonable lengths to deliver on goals

Nice To Haves

Previous experience working on full-stack AI projects is a plus
Familiarity with or a desire to learn our stack is a plus (Python, React, Typescript, PostgreSQL, and Kubernetes)

Responsibilities

Decipher and automate complex, branching workflows for insurance coverage, affordability programs, and fulfillment
Combining AI/ML approaches to achieve high precision document classification, unstructured data extraction, and reference-based question answering
Automating multi-step, path-dependent processes, using a combination of RPA/scraping approaches to navigate and operate third-party platforms
Building a state machine that drives system decisions and handles failure modes across a set of processes that are technically independent but practically intertwined
Scale across a growing range of drug classes, patient populations, and provider markets
Making our data and ML pipelines robust to variation and inconsistency in input data formats (e.g., clinical documentation structure and style)
Leveraging empirical data to build and continuously update our understanding of opaque external systems (e.g., insurance company policies)
Creating consumer-grade experiences for patients, physicians, and other users that incorporate intuitive AI-powered workflows
Use our network to help biopharma partners accelerate drug development, launch, and access
Translating large volumes of heterogeneous data into reliable insights, informing decisions like clinical indication selection, launch markets, and insurer negotiations
Developing predictive and simulation models to forecast outcomes such as clinical trial site performance, drug adoption rates, and the impact of rebates/subsidies
Using real-time data and direct engagement channels to enroll criteria-matching patients and physicians in clinical studies and access programs