;Dros builds AI-powered outbound voice agents for debt collections. Our platform autonomously makes phone calls, identifies the right customer, holds natural conversations, collects payments, sends follow-up SMS messages, and records every interaction all while operating within strict regulatory requirements.
;Machine learning sits at the heart of our product. From powering conversational intelligence to improving call outcomes and enabling smarter automation, our models directly influence how effectively our AI agents interact with customers in real-world conversations.
; Position Summary
;As we onboard more customers and expand our platform, were investing heavily in the intelligence that powers our AI agents. Youll help build the next generation of machine learning systems that improve conversation quality, decision making, and operational efficiency at scale.
;This is an opportunity to work across the entire ML lifecycle from defining business problems and designing experiments to deploying, monitoring, and continuously improving production models.
Youll have the freedom to influence both our ML platform and the products it enables.
; Key Responsibilities
- ;Design, build, and maintain production machine learning systems that power customer-facing AI capabilities.
- ;Train, evaluate, and optimize machine learning models, selecting the right approach from traditional statistical methods to contemporary deep learning architectures based on business needs and operational constraints.
- ;Partner with product managers, engineering teams, and business stakeholders to translate real-world problems into measurable machine learning solutions.
- ;Design experiments, evaluate model performance, and use data-driven insights to influence product direction.
- ;Build scalable feature engineering, training, and inference pipelines for both batch and real-time workloads.
- ;Work closely with platform and backend engineers to integrate ML models into production services with relia