About Company
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, we're investing heavily in the intelligence that powers our AI agents. You'll 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. You'll 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 modern 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 reliability, observability, and maintainability in mind.
- Continuously improve model quality through monitoring, retraining, and iterative experimentation.
- Mentor other engineers and help establish engineering best practices around code quality, testing, documentation, and operational excellence.
Our Engineering Culture
We're a small engineering team where machine learning engineers own solutions end-to-end—from understanding the business problem through experimentation, deployment, monitoring, and continuous improvement.
We Value
- Building models that solve real customer problems rather than optimizing benchmark scores.
- Strong engineering fundamentals alongside strong machine learning expertise.
- Data-driven decision making and measurable business impact.
- Simple, maintainable solutions over unnecessary complexity.
- Collaborative technical discussions and continuous learning.
- High standards for testing, monitoring, and production reliability.
Our Technology Stack
- Languages: Python, SQL
- Machine Learning: PyTorch, TensorFlow, Keras
- Cloud: Google Cloud Platform (GCP)
- Data: BigQuery, Kafka, Spark, modern data processing pipelines
- ML Operations: Automated training pipelines, model monitoring, retraining, experimentation
- Development: GitHub Actions, automated testing, CI/CD
We're pragmatic about technology. Experience with equivalent tools and cloud platforms is equally valuable.
What We're Looking For
We're looking for engineers who have successfully built and operated machine learning systems in production.
You'll Likely Have Experience With
- 5–7 years of applied machine learning experience using Python.
- A strong understanding of machine learning fundamentals, modern deep learning techniques, and when to apply different modeling approaches.
- Building, deploying, and maintaining production ML systems in fast-moving environments.
- Designing experiments and analyses that influence product decisions and business outcomes.
- Working with deep learning frameworks such as PyTorch, TensorFlow, or Keras, with an understanding of how they work beyond simply using their APIs.
- Building scalable data pipelines and working with large datasets using technologies such as BigQuery, Kafka, Spark, Hadoop, or similar platforms.
- Applying MLOps best practices including automated testing, model versioning, monitoring, retraining, and production observability.
- Quickly understanding new business domains and translating ambiguous problems into practical machine learning solutions.
- Working collaboratively within agile engineering teams and adapting to changing priorities.
- Experience building and operating machine learning workloads on Google Cloud Platform.
Nice to Have
Experience With Any Of The Following Is a Bonus
- Large Language Models (LLMs) and generative AI applications.
- Conversational AI, speech technologies, or voice applications.
- Recommendation systems, ranking models, or personalization.
- Production feature stores and online inference systems.
- Distributed model training and optimization.
- Building evaluation frameworks for AI systems.
You'll Enjoy This Role If You...
- Like solving real business problems using machine learning rather than building models in isolation.
- Enjoy taking ownership of ML systems from experimentation through production.
- Care about model reliability, maintainability, and measurable business impact.
- Prefer working closely with product and engineering teams to build end-to-end solutions.
- Want to influence the direction of both the ML platform and the products it powers.
- Enjoy working in a small team where your ideas and contributions have a direct impact.
