Kard is building commerce media infrastructure that connects financial institutions, financial-services platforms, and merchants through data and merchant-funded rewards. As our reach expands, we are building for a step-change in scale, and for opportunities ranging from more relevant rewards and marketer intelligence to new commerce and distribution models.

About the Role

As a Senior Machine Learning Engineer, you will design, build, and operate the production systems that turn machine learning into durable product capabilities. Your scope will span personalization and recommendations, optimization and decisioning, experimentation and measurement infrastructure, model training and serving, and the shared platforms and tooling that support ML development at Kard.

This is a senior individual-contributor role on the Data team, reporting to the Director of Data & Platform. You will work primarily with other machine learning engineers and data scientists, while partnering across Data and Product to move models from experimentation through deployment and ongoing operation. You will also provide technical leadership through architecture, engineering standards, mentorship, and design and code review.

Responsibilities

• Design, build, and operate end-to-end machine learning systems across batch and real-time workloads, including feature generation, training, evaluation, deployment, inference, and retraining.
• Develop production capabilities for personalization, recommendations, ranking, optimization, contextual decisioning, experimentation, and measurement.
• Build reusable ML platforms, pipelines, frameworks, and tooling that improve reproducibility, self-service, reliability, and development speed.
• Own the operational lifecycle of production models, including observability, data and model quality, drift detection, performance monitoring, failure handling, retraining, and governance.
• Partner with other ML practitioners to turn prototypes and research into maintainable systems, and with Data and Product teams to integrate those systems into Kard’s products and platform.
• Provide technical leadership for ML architecture and distributed systems, making pragmatic tradeoffs among model performance, reliability, scalability, development speed, and operational complexity.

Desired Skills

• 8+ years of professional software engineering or machine learning engineering experience.
• A strong record of building, deploying, and operating machine learning systems in production.
• Strong software engineering fundamentals, including system design, APIs, testing, deployment, observability, and maintainable production code.
• Experience designing distributed systems, large-scale data-processing workflows, or high-throughput and low-latency services.
• Fluency across the ML lifecycle, including data and feature pipelines, training, evaluation, model serving, monitoring, and retraining.
• The ability to investigate production issues across models, data, application code, and infrastructure.
• The judgment and autonomy to lead technically complex work, navigate ambiguity, and make architectural decisions with long-term consequences.
• Strong collaboration and communication skills, particularly when working across machine learning, data engineering, analytics, and product disciplines.
• A commitment to raising the technical capabilities of the broader team through mentorship, thoughtful review, documentation, and shared engineering practices.
• Demonstrated experience matters more to us than a particular degree or industry background.
• U.S. core business hours availability and willingness to travel for company meetings.

Bonus Points

• Experience with personalization, recommendation, ranking, optimization, experimentation, measurement, or real-time decisioning systems.
• Experience with technologies such as Python, Apache Spark, Databricks, MLflow, feature stores, or model-serving platforms.
• Experience operating ML workloads on AWS, Kubernetes or Amazon EKS, and infrastructure managed through Terraform.
• Experience building ML systems at enterprise, transaction-intensive, or regulated scale.