Company Overview
Promise empowers utilities and government agencies to create flexible, affordable solutions for individuals struggling with debt. Our innovative approach to payment plans and relief distribution significantly improves enrollment and recovery rates, helping individuals clear debts faster and reducing delinquencies for our partners.
We treat people facing financial difficulties with respect and dignity, providing the tools and resources they need to thrive. Our team includes experts from companies like Palantir, Google, Stripe , and esteemed government leaders.
Backed by over $50 million in funding from top investors – such as Reid Hoffman, Howard Schultz, Michael Seibel, Y Combinator, 8VC, The General Partnership, First Round Capital, Kapor Capital, XYZ Ventures, and Bronze Investments – Promise has been recognized as one of Fast Company's "World's Most Innovative Companies of 2022,” “Forbes Next Billion-Dollar Startups 2024,” and Y Combinator’s #1 GovTech startup.
As a Senior ML / AI Researcher at Promise, you’ll drive the exploration and development of AI and machine learning models to streamline the way individuals facing financial difficulty receive and interact with essential public services.
Your primary responsibilities will include experimenting with, training, fine-tuning, and prompting AI systems - ranging from traditional ML models to LLMs - to power our internal and external applications. This work will span areas like OCR, document understanding, document classification, and building generalist models. You’ll source from leading AI vendors, open-source models, and fine-tuned solutions, applying them to real-world use cases that impact millions of people.
Your expertise in AI modeling will help improve performance, efficiency, and automation across our platform - directly shaping the future of government services while delivering meaningful social impact.
5+ years of relevant statistical analysis experience.
5+ years of professional machine learning experience (e.g., modeling, hyperparameter tuning, feature engineering, model validation).
1+ years of experience with deep learning and LLMs (hands-on projects, fine-tuning, or applied usage).
Formal experience training and evaluating models for classification/regression tasks (ROC AUC, Precision, Recall, F1 score).
Exposure to key tools and frameworks such as PyTorch, MLFlow, LightGBM, or related.
A rigorous, detail-oriented approach to problem solving.
Willingness to learn, change, and grow in a high-impact, dynamic environment.
Passionate drive for Promise’s mission and relentless pursuit of customer success.
High moral and ethical standards, seeking open and honest communication, and rejecting manipulation, dishonesty, and intolerance.
At Promise, we invest in our team’s well-being, growth, and sense of ownership.
Equity for All: All full-time employees receive stock options to share in our company’s success.
100% Paid Health Coverage: We cover 100% of base medical, dental, and vision insurance plans for employees and their dependents.
Flexible Hybrid Work: We collaborate in office at least three days a week to stay connected and aligned as a team.
Please note: Benefits are reviewed periodically and may be updated at the sole discretion of Promise.
Promise is an equal opportunity employer and does not discriminate against any applicant or employee because of race, color, religion, sex, sexual orientation, gender identity, national origin, disability, genetic information, age, or military or veteran status. Additionally, the Company complies with applicable state and local laws governing non-discrimination in employment in every jurisdiction in which it operates. Promise is committed to promoting diversity and inclusion in the workplace. We also provide reasonable accommodations to qualified individuals with disabilities, pregnant individuals, and those with sincerely held religious beliefs, in accordance with applicable laws.
Promise engages in US government contracts and restricts hiring to US persons, which includes US citizens and permanent residents (e.g., Green Card holders). Additionally, candidates must reside in the US.
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