Why you should join us

Tandem is a generational opportunity to rethink how we bring new therapies to market, and our path to doing so is significantly de-risked – we have:

• Exponential organic growth: We have product-market fit and are growing rapidly through word-of-mouth. Tandem supports thousands of patients every day, is doubling doctor users every quarter, and is working with the largest biopharma companies in the world.

• An AI-first business model: Our approach is distinctly enabled by AI, but our business will get stronger (not commoditized) as foundation models improve. We are building durability through two-sided network effects that will compound over time.

• Top tier investors: With the traction to support conviction in our model, we raised significant funding from investors (including Thrive Capital, General Catalyst, Bain Capital Ventures, and Pear VC) to build an exceptional team of engineers and operators.

Our number one priority is scaling to market demand. We are looking for individuals who are high horsepower, high throughput, and hyper resourceful to help us increase capacity and grow. We move fast and need to move faster.

All full-time roles are in person in New York. You can learn more about working with us in the last section of this page.

About the role

As a Machine Learning Engineer on our team, you will work on real production use cases of LLMs and other ML techniques to solve business problems and create groundbreaking AI applications. The role requires that you develop a deep understanding of our product surface area and what drives our business, such that you can operate and drive impact both cross-functionally and independently. You will have end-to-end responsibility for projects, including definition, design, development, launch, and success––this includes ensuring your output has the expected impact on user growth, operational efficiency, or revenue generation.

This is a demanding role, with a high level of autonomy and responsibility. You will be expected to "act like an owner" and commit yourself to Tandem's success. If you are low-ego, hungry to learn, and excited about intense, impactful work that drives both company growth and accelerated career progression, we want to hear from you.

If you join, you will:

• Scope and spearhead AI augmentation and automation projects across our product surface area, including: Unintuitive classifications, Data extraction and summarization, Precise content generation, Reference-based search and question answering, Process outcome prediction, Probabilistic triggering of workflows, and Multimodal model-powered bots

• Drive zero-to-one product development from conceptualization through production, collaborating with our go-to-market and operations teams

• Stay on top of emerging AI methods and drive decisions around which models and techniques we use, including where we fine-tune and train models

• Establish research strategies for various AI methods, including experimentation and evaluation protocols that control for both accuracy and consistency

• Prototype and productionize AI functionality and agents, incorporating real-world feedback to refine them

• Participate actively in client engagements, working directly with customers to understand requirements and deliver innovative solutions

• Develop engineering process, tools, and systems to support faster AI product development (e.g., build a one-click eval system) and scaling (e.g., model invocation efficiency)

• Work closely with the rest of our team and CEO to make business decisions as we balance speed of growth and long-term profitability

We need your help to:

• 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)