Base Career helps you apply smarter for this job.
Key skills for this role
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.
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.
Skip the repetitive application forms
Install the Base Career Chrome Extension and autofill job applications across major job boards with your profile.
Trusted by over 500,000 job seekers on Base Career
More from this employer
, USA
, USA
New York City, USA
, USA
, USA
, USA
New York City, USA
New York City, USA
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.
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 Sampling, UCB), and Bayesian inference. You know when to use a simple regression and when to deploy a DDQN.
Strong Programming: Expertise in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, Boosted Trees, and Scikit-Learn. Experience with SQL and data manipulation is required.
Cloud Native: Experience with ML cloud platforms such as AWS Sagemaker, Databricks, or similar. You are comfortable building your own deployment pipelines.
Scientific Rigor: You have a strong background in experiment design, A/B testing, and causal inference. You understand that a model is only as good as the experiment that validates it.
Curiosity & Grit: You are willing to look at "messy" operational data (complaints, collections logs) and find the mathematical structure within it.
Uncapped Impact: You will be a catalyst for our healthy and growing business, directly influencing the bottom line by optimizing our core operational engines.
Innovation: We are building a product that reimagines the way money moves, and we are doing it by applying cutting-edge ML to problems that competitors solve with spreadsheets.
Growth: We believe in empowering our people to be successful. This role offers a clear path to leadership and the chance to shape the technical direction of the company.
Fintech providing merchants with tailored point-of-sale credit and pay-over-time financing for consumers.
Visit company websiteJobs and hiring trendsUSD 125000-150000 yearly / year
Full-time
Senior · 1+ years experience
Remote
Apply faster on company sites with our extension.