Senior Machine Learning Engineer

Amperity•Seattle, WA

4d•Hybrid

About The Position

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 Senior ML Engineer, you'll lead complex, ambiguous ML projects within your team's space and own technically deep pieces of its ML architecture. You'll create agreement and simplicity on your team. Being an ambassador for your work, you'll collaborate across team lines. Partnering with Applied Scientists, Software Engineers, and Product Managers, you'll 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—using them to accelerate development and improve code quality. We keep our processes lightweight, our experimentation rigorous, and our focus on delivering value to our customers through machine learning products and features. Interesting Problems We're solving tough problems at the intersection of large-scale data, AI, and user experience. Some of the challenges you might work on include: Design the CI/CD pipelines and deployment architecture for the ML systems your team owns, making them reliable, repeatable, and easy to operate. Build automated retraining pipelines triggered by performance degradation, and architect monitoring solutions with drift detection and alerting. Design real-time and batch feature pipelines that power identity resolution, customer segmentation, and predictive models at scale. Improve model inference latency to deliver predictions that meet strict Service level agreements while keeping infrastructure costs in check. Establish SLOs and operational standards for your team's production ML. Lead incident response and blameless post-mortems. Evaluate MLOps tooling that raises the bar for the team, including experiment tracking, model registry, and serving. About You You're a ML engineer who pairs deep technical judgment with the ability to build and operate production systems end-to-end. You own technically complex pieces of your team's ML architecture. Your teammates seek you out for advice in your space. You ramp quickly in unfamiliar areas—often leaning on AI tools to do it—and you embrace AI-first practices, helping establish how your team works with tools like Claude Code. You value simplicity, mentorship, and well-reasoned decisions. 5+ years building production ML systems, including hands-on experience designing ML pipelines and infrastructure. Experience leading complex or ambiguous ML projects within a team as the directly responsible individual. 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. A habit of mentoring teammates, reviewing others' work, and elevating how your team builds and operates ML systems. Technologies To Know We don't expect you to have experience with everything we use—but if you're excited about learning, you'll do great here. You'll 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.

Requirements

5+ years building production ML systems, including hands-on experience designing ML pipelines and infrastructure.
Experience leading complex or ambiguous ML projects within a team as the directly responsible individual.
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.
A habit of mentoring teammates, reviewing others' work, and elevating how your team builds and operates ML systems.

Nice To Haves

Experience with large-scale data engines like Apache Spark, Presto, and Kafka.
Experience with MLOps tooling including MLflow, feature stores, and model serving frameworks.
Experience with cloud-native infrastructure built with Kubernetes and Terraform, deployed across multiple cloud providers.
Experience with functional programming languages including Clojure and Python for ML pipelines.
Experience with machine learning models for entity resolution, classification, and customer analytics.
Experience using AI coding assistants (such as Claude Code) as part of our daily development workflow.

Responsibilities

Lead complex, ambiguous ML projects within your team's space and own technically deep pieces of its ML architecture.
Create agreement and simplicity on your team.
Collaborate across team lines as an ambassador for your work.
Partner with Applied Scientists, Software Engineers, and Product Managers to deliver production ML systems that create measurable customer impact.
Design the CI/CD pipelines and deployment architecture for the ML systems your team owns, making them reliable, repeatable, and easy to operate.
Build automated retraining pipelines triggered by performance degradation, and architect monitoring solutions with drift detection and alerting.
Design real-time and batch feature pipelines that power identity resolution, customer segmentation, and predictive models at scale.
Improve model inference latency to deliver predictions that meet strict Service level agreements while keeping infrastructure costs in check.
Establish SLOs and operational standards for your team's production ML.
Lead incident response and blameless post-mortems.
Evaluate MLOps tooling that raises the bar for the team, including experiment tracking, model registry, and serving.