Senior Machine Learning Engineer

Senior Machine Learning Engineer

About Amperity—Why This, Why Now

Most customer data is a lie by omission.

Every consumer brand wants to know its customers. Unfortunately, almost none actually do. Data is scattered across a dozen systems, half of it contradicts the other half, and "insights" teams are forced to act on incomplete guesses. We started Amperity ten years ago because that gap—between what a brand believes about its customers and what is true—is the most expensive problem in the industry, and nobody had solved it.

With a lot of hard work and a little bit of luck, we did. Our patented identity resolution takes billions of messy, conflicting records and resolves them into an accurate picture of a real human being—in hours, not months. That’s not a feature; it’s the foundation everything else stands on. It’s why more than 400 brands—like Alaska Airlines, Wyndham Hotels & Resorts, DICK’S Sporting Goods, Virgin Atlantic, and Brooks Running—trust us with the one thing they can’t afford to get wrong, and why IDC named us a Leader in the category in 2026.

Now we’re doing something harder. We’re rebuilding Amperity as an AI-first company—not bolting a chatbot onto old software, but putting agents at the center of how customer data actually gets used. We shipped the industry’s first identity resolution agent, then the first enterprise Customer Data Agent that turns a plain-language question into a live segment without waiting on an engineering queue. The bet underneath all of it is simple, and we think it’s correct: The potential of AI will not be realized for a brand until it’s grounded and activated against a true, unified view of the customer. The market at-large is focused on intelligence, and we are focused on being the ones who can make it trustworthy.

So here’s the honest pitch. If you want a tour of a category that’s already been figured out, this isn’t it. If you want to help decide what the next version of an entire industry looks like—alongside people who are remarkably exceptional at what they do, solving a problem that requires deep, sustained focus—this is one of the few places that work exists. Our goal is to build a highly impactful, generational business.

We measure our success by one standard: whether what we build moves the business. Return on cognition, not return on hype. If that’s how you want to be measured, we would love to speak with you.

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 ClaudeCode. 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.
• ...