Role Overview

We're seeking a passionate and experienced SW Engineer to join the Revenue Optimization ML team. This high-impact role combines infrastructure expertise with applied MLOps knowledge, requiring proficiency in software engineering, distributed systems, and machine learning operations.

Key Responsibilities

• Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments.
• Build and maintain efficient data pipelines for large-scale processing and feature engineering.
• Optimize compute resources and improve model serving performance across ML systems.
• Implement robust monitoring, logging, and alerting systems, and contribute to ML Ops practices including CI/CD pipelines.
• Collaborate closely with Data scientists to streamline the model development-to-production workflow.
• Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders.

Qualifications

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field.
• 4-6 years of experience in Software, Data, or ML engineering roles (preferred).
• Strong problem-solving skills, proficiency in Java, Kotlin or Scala and desirable Python.
• Understanding of ML algorithms, model architectures, and experience building scalable, reliable ML systems.
• Exposure to cloud platforms (e.g., AWS), containerization (Docker), and scalable data systems (e.g., Spark, Kafka).
• Familiarity with CI/CD tools (e.g., GitHub Actions), ML model serving technologies (e.g., MLflow), and ability to collaborate well across teams.

Ideal Candidate

This role is well-suited to someone with a solid foundation in at least one of the following areas: software engineering, data engineering, distributed systems, or applied machine learning and a readiness to grow into others as the work requires. We welcome candidates who are strong, language-agnostic problem solvers and passionate about building robust ML systems, even if you don't meet every single requirement listed.

How to Apply

If you believe you can do the job and are excited to grow with us, we'd love to hear from you. Please submit your application through our careers page.

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