Applied AI Engineer

Forus•New York, NY

7d•Onsite

About The Position

As an Applied AI 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 Forus' 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.

Requirements

Strong programming skills and general Computer Science knowledge
Experience driving AI projects end-to-end — from model development and data infrastructure to production deployment and real-world iteration
Proficiency with modern AI/ML technologies (e.g., LLM APIs, PyTorch, TensorFlow) and a strong foundation in full-stack web development (we use Python, React, TypeScript, PostgreSQL, and Kubernetes)
Strong written and verbal communication that allows you to be an effective participant in both internal debates and external relationships
Track record of moving quickly, finding shortcuts, and going to unreasonable lengths to deliver on goals
High NPS with your former teammates

Nice To Haves

If you don't meet every single one of them, you should still consider applying!
We’re excited to work with people from underrepresented backgrounds, and we encourage people from all backgrounds to apply.

Responsibilities

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
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