At Amperity, we’re an AI-first company helping the world’s leading brands create personalized customer experiences that build loyalty and fuel growth. Our AI-powered Customer Data Cloud, built on multi-patented technology, enables more than 400 global brands, including Alaska Airlines, Wyndham Hotels & Resorts, and DICK’S Sporting Goods, to turn customer data into a competitive advantage.
We unlock the full value of customer data with simplicity and speed. AI is at the core of our platform and the way we work — from powering advanced identity resolution and predictive analytics to streamlining internal workflows and decision-making. It’s not just a capability; it’s part of our DNA.
Our team thrives on curiosity, collaboration, and transparency, fostering a culture where everyone can contribute, learn, and grow. We welcome talented individuals from diverse backgrounds to help us remove data bottlenecks, accelerate business impact, and push the boundaries of what AI can do for the world’s most innovative companies.
With offices in Seattle, New York City, London, and Melbourne, you’ll join a fast-growing team tackling critical challenges at the intersection of AI, data, and customer experience. Ready to make an impact? Let’s talk.
The Role
At Amperity, ML Engineers work in small, collaborative, and accountable teams. As a Lead ML Engineer, youll lead ML projects that span multiple teams, guiding both the technical direction and the platform capabilities that power our AI-driven products. Youll work with Applied Scientists, Software Engineers, Product, and Customer Success teams to deliver production ML systems that create measurable customer impact. We are an AI-first company. We expect engineers to embrace AI assistance tools like Claude Code as a core part of their daily workflow. They use these tools to accelerate development, improve code quality, and velocity. We keep our processes lightweight, our experimentation rigorous, and our focus on delivering value through machine learning.
Interesting Problems
Were solving tough problems at the intersection of large-scale data, AI, and user experience. Some of the challenges you might work on include:
• Architect ML platform components—feature stores, model registries, and serving infrastructure—that help teams across the organization to deploy models reliably and at scale.
• Build automated training and deployment pipelines that support model improvement for data drift and model degradation.
• Design real-time and batch feature engineering systems that power identity resolution, customer segmentation, and predictive models at enterprise scale.
• Improve model inference latency to deliver ML predictions that meet strict Service level agreements while managing infrastructure costs.
• Establish MLOps best practices, SLOs, and operational standards that ensure production ML systems are reliable, observable, and maintainable.
About You
Youre a technical visionary who combines deep ML engineering expertise with systems building and experience building infrastructure that supports and scales several types of machine learning models. You take ownership of ML systems end-to-end—from model development through production operation—and help set the technical direction for ML engineering across the organization. You embrace AI-first practices, using tools like Claude Code to accelerate your work and advocating for their adoption across teams. You value influence, mentorship, and well-reasoned decisions.
• 8+ years of experience building production ML systems, with experience designing ML infrastructure and platforms.
• Technical leadership experience driving ML platform evolution or major ML projects across multiple teams.
• Expertise in ML deployment patterns, model serving, feature engineering, and monitoring/observability for ML systems.
• Software engineering skills with experience in Python and familiarity with ML frameworks (e.g. XGBoost, PyTorch, PySpark).
• Experience with cloud-native ML infrastructure, containerization, and orchestration (Kubernetes, Docker).
• Enthusiastic about AI-first development practices, with experience using AI coding assistants to accelerate engineering workflows.
• Turn ambiguous ML infrastructure problems into actionable plans, and guide teams through delivery.
• Experience aligning ML technical strategy with our priorities and customer needs.
• Mentor engineers, and improve how we build and operate ML systems.
Technologies To Know
We dont expect you to have experience with everything we use—but if youre excited about learning, youll do great here. Youll influence and learn:
• Large-scale data engines like Apache Spark, Presto, and Kafka.
• MLOps tooling including MLflow, feature stores, and model serving frameworks.
• Cloud-native infrastructure built with Kubernetes and Terraform, deployed across multiple cloud providers.
• Functional programming languages including Clojure and Python for ML pipelines.
• Machine learning models for entity resolution, classification, and customer analytics.
• AI coding assistants (such as Claude Code) as part of our daily development workflow.