Junior Research Scientist
EliseAINew York City$150k – $230kPosted 9 March 2026
Job Description
Junior Research Scientist
About EliseAI
At EliseAI, we're improving the industries that matter most: housing and healthcare. Everyone needs a place to live and access to quality healthcare, yet both are often harder to secure than they should be.
By integrating AI agents deeply into existing workflows, we make them more efficient, reduce costs, and improve the experience for everyone.
- Housing: We simplify how renters tour apartments, sign leases, submit maintenance requests, and stay connected with their property team—bringing everything they need for their home into one place.
- Healthcare: We make it easy to schedule appointments, complete intake forms, and we help patients communicate with providers, so everyone can focus on health instead of paperwork.
With EliseAI, organizations reduce manual work, improve accessibility, and deliver a seamless experience across essential services. We recently raised a $250 million Series E round https://www.eliseai.com/blog/eliseai-raises-250m-series-e led by Andreessen Horowitz to accelerate this mission.
About the Role
EliseAI invites top-tier professionals in Machine Learning and adjacent disciplines to join our team. This position is perfectly suited for individuals with a robust mathematical and quantitative foundation—this includes, but is not limited to, mathematicians, theoretical physicists, and computer scientists—who are eager to apply their knowledge to tangible challenges. As a junior research scientist at EliseAI, you will join a fast-moving team of world-class researchers engaged in groundbreaking research on machine learning and large language models, developing predictive models to advance our AI capabilities, and much more. If you thrive in a vibrant startup setting and are keen to apply your academic expertise to innovative applications, we encourage you to connect with us.
Note: For this position, we will conduct a 1-month trial period before transitioning to full-time employment.
Key Responsibilities
- Develop and refine machine learning architectures optimized for advanced conversational flow
- Conduct comprehensive analyses to support decision-making and improve the effectiveness of our AI solutions
- Apply advanced mathematical and machine learning techniques to solve specific problems such as optimizing tenant engagement strategies, predicting tenant behavior, and improving customer interaction processes
- Stay abreast of the latest research in machine learning and AI, and apply innovative approaches to improve our products
- Work closely with cross-functional teams, including engineering, product development, and data science, to integrate and deploy machine learning models
- Work with the latest Large Language Models to leverage the power of generative AI on production scale data
Move at rocket speed, build something massive.
We’re scaling fast, solving real client problems with precision and ambition. Here, you own your impact; full autonomy, no micromanagement, no fluff. We hire the best, expect the best, and give you the masterclass of your career. It’s hard, it’s intense, and it’s the most rewarding work you’ll ever do. If you’re hungry, driven, and ready to build something massive, climb aboard.
Requirements
- Ph.D. in Mathematics, Theoretical Physics, Computer Science, or a related field
- Strong foundation in machine learning algorithms, statistical analysis, and data modeling
- Demonstrated ability to apply analytical thinking and problem-solving skills to real-world challenges
- Proficiency in programming languages such as Python, R, or similar, with experience in machine learning libraries and frameworks
- Willingness to work in person at our office 4-5 days a week
Why Join
Growth and impact. It’s not often that you can get in on the ground floor of a funded (unicorn! https://www.eliseai.com/blog/eliseai-raises-250m-series-e) startup that’s scaling so fast. That means that instead of following a playbook, you’ll be writing i ... (truncated, view full listing at source)
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