About In-House Health

In House Health is a seed stage startup that aims to help the global healthcare crisis marked by severe staffing shortages and rising nurses burnout. In-House Health develops a cutting-edge platform that empowers nursing teams to self-manage with unprecedented flexibility. Our innovative solution leverages a predictive AI algorithm to optimize nurse shift assignments.

In-House Health is backed by one of the largest VCs in the world, and the CEO is Ari Brenner, who previously founded a digital health company with a >$1B valuation

About the Role

We're looking for a Senior Algorithm Engineer to join our team. In this role, you'll be responsible for building, improving, and maintaining our core data-driven products. This isn't a traditional data science role; instead, you'll be focused on leveraging classical machine learning, statistical methods, and various AI techniques to build robust solutions that solve real-world problems.

Responsibilities include:

• Predictive modeling: Develop predictive algorithms and statistical models to solve complex healthcare operational challenges
• Data analysis and insights: Analyze complex healthcare data to identify patterns, trends, and opportunities for optimization.
• AI-driven solutions: Use large language models (LLMs) and other AI tools to build and deploy systems that enhance communication and automate tasks.
• Full lifecycle ownership: Take full responsibility for your projects, from initial research and algorithm development to owning the backend (BE) implementation and ongoing maintenance.
• Collaboration: Work directly with the VP of R&D and other team members to translate business needs into technical solutions.

About You

• You are highly motivated and excited to work at an early-stage company.
• You have 3+ years of experience in a similar role (e.g., algorithm engineer, machine learning engineer, or data scientist).
• You have a strong understanding of classical machine learning algorithms, statistical analysis, and data modeling.
• You have hands-on experience with Python and relevant libraries (e.g., NumPy, Pandas, Scikit-learn).
• You can quickly translate ideas into working solutions and enjoy building new products from the ground up.