Senior Machine Learning Engineer

New York Cityfull-timesenior

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

End-to-End Problem Solving: Own the full lifecycle of the solution. You will dive deep into case reviews to formulate hypotheses, design rigorous A/B tests to validate them, and automate the scaling of successful strategies.

Requirements Summary

Experience: 1-5+ years of industry machine learning experience with excellent engineering skills. RL & Optimization Expertise: Strong theoretical understanding and practical experience with Reinforcement Learning (RL), Bandit algorithms (Thompson…

Technical Tools

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We give businesses and their customers peace of mind by solving complex credit challenges with precision, speed, and intelligence, combining deep expertise with advanced technology, to simplify the experience and deliver better outcomes, every time.

We're a fast-growing fintech empowering enterprise merchants with smarter, more adaptive pay-over-time solutions. From point-of-sale financing to “Buy Now, Pay Later” programs and loyalty integrated offers, we’re building configurable credit tools that help businesses serve more of their customers.

We value teamwork, clarity of purpose, and rigorous attention to data to drive action. We balance speed and excellence to deliver an exceptional customer experience.

ClarityPay is undertaking transformative investments in machine learning products, algorithms, and platforms. We are building a team of technically proficient, hands-on engineers who are passionate about solving complex optimization problems across customer complaints, collections, and offer optimization.

This role is for the engineer who looks at a "collections process" and sees a Reinforcement Learning environment. You will engage directly with the problem space—performing deep case reviews to understand the "why" and "what"—and develop rigorous hypotheses to optimize outcomes. You will move beyond simple predictive models to build transformative algorithmic solutions using Bayesian Black Box optimization, Contextual Bandits, and Deep Q-Networks (DQN/DDQN).

The problem space here is ripe for innovation. Your curiosity, drive, and aptitude will determine the ceiling of your impact. You will have the opportunity to expand into leadership responsibilities, including technical mentorship and management of offshore engineering teams.