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Salary: USD 160,000 - 190,000 / annual
Kard is building commerce media infrastructure that connects financial institutions, financial-services platforms, and merchants through data and merchant-funded rewards. As a Senior Machine Learning Engineer, you will design, build, and operate production machine learning systems that power Kard's product capabilities.
You will work as a senior individual contributor on the Data team, reporting to the Director of Data & Platform. Your scope spans personalization and recommendations, optimization and decisioning, experimentation and measurement infrastructure, model training and serving, and shared ML platforms and tooling. You will partner with machine learning engineers, data scientists, and product teams to move models from experimentation through deployment and ongoing operation.
Key responsibilities include designing and operating end-to-end ML systems across batch and real-time workloads, including feature generation, training, evaluation, deployment, inference, and retraining. You will develop production capabilities for personalization, recommendations, ranking, optimization, contextual decisioning, experimentation, and measurement. You will build reusable ML platforms, pipelines, frameworks, and tooling to improve reproducibility, self-service, reliability, and development speed.
You will own the operational lifecycle of production models, including observability, data and model quality, drift detection, performance monitoring, failure handling, retraining, and governance. You will provide technical leadership for ML architecture and distributed systems, making pragmatic tradeoffs among model performance, reliability, scalability, development speed, and operational complexity. You will mentor other ML practitioners and contribute to raising technical capabilities across the team through thoughtful review, documentation, and shared engineering practices.
Required qualifications include 8+ years of professional software engineering or machine learning engineering experience, a strong record of building and operating ML systems in production, and strong software engineering fundamentals including system design, APIs, testing, deployment, observability, and maintainable code. You should have experience designing distributed systems, large-scale data-processing workflows, or high-throughput and low-latency services, and fluency across the ML lifecycle. You must be able to investigate production issues across models, data, application code, and infrastructure, and have the judgment and autonomy to lead technically complex work and make architectural decisions with long-term consequences.
Bonus experience includes personalization, recommendation, ranking, optimization, experimentation, measurement, or real-time decisioning systems; technologies such as Python, Apache Spark, Databricks, MLflow, feature stores, or model-serving platforms; operating ML workloads on AWS, Kubernetes, or Amazon EKS; and building ML systems at enterprise, transaction-intensive, or regulated scale. U.S. core business hours availability and willingness to travel for company meetings required.
