About the Team
We are building the alarm system for e-commerce operations.
Our mission is to detect and disrupt the risks—from delivery fraud to "Item Not Received" (INR) claims—that create friction between merchants, carriers, and 3PLs. We sit at the intersection of e-commerce, logistics, and fraud, turning massive, complex data streams into the clear-cut intelligence our clients need to operate safely.
We're a growing "pod" of a Data Scientist (the "brain") and a Data Engineer (the "infrastructure"). We are now looking for the critical third member: the Machine Learning Engineer who will build and deploy our intelligence into production.
About the Role
As our Founding Machine Learning Engineer, you will be the "builder" who productionizes our intelligence. This isn't a role for R&D; this is a role for building, shipping, and scaling our models.
You will be the hands-on partner to our Data Scientist. They will come to you with a model prototype and a "why"; you will be the one who knows "how" to build it into a low-latency, high-availability, production-grade service.
Your challenge is to build the MLOps foundation and the APIs that are our product. You will own the full lifecycle of our models, from deployment to monitoring, ensuring our "alarm system" is always-on, fast, and reliable.
In this role, you will:
Own the deployment lifecycle: Partner with our Data Scientist to take model prototypes and build, deploy, and scale them in a production environment.
Build production services: Design and build the robust, low-latency APIs that serve our model's predictions to our core platform.
Establish our MLOps foundation: You will be the owner of our MLOps tooling, building the systems for CI/CD, training, and monitoring.
Optimize for performance: Obsess over model speed, reliability, and accuracy. You'll be responsible for ensuring our models can handle real-time, high-volume traffic.
Write production-grade code: You are a software engineer first, who specializes in ML. You will write clean, testable, and maintainable code.
Partner with the Data Engineer to define the data-serving requirements for your models, ensuring the "infrastructure" and "intelligence" are perfectly in sync.
You might thrive in this role if you:
Have 4+ years of hands-on experience as a Machine Learning Engineer or Software Engineer (with a focus on ML).
Are fluent in Python and have deep experience in building and deploying ML models (e.g., scikit-learn, TensorFlow, PyTorch).
Are a production-first builder. You have experience building and managing real-time APIs (e.g., FastAPI, Flask) and containerization (e.g., Docker, Kubernetes).
Have hands-on experience with MLOps tooling and cloud platforms (especially GCP).
Are obsessed with reliability and automation. You believe that if a model isn't monitored and automated, it isn't "done."
Are a "builder" who "shows, not tells." You prove how good you are by shipping elegant, working solutions, not by talking about them.
Thrive on autonomy and ownership. You want "guidance, not guardrails." We'll give you a problem, and we know you'll find a smart way to solve it.
Are a listener and a collaborator. You know the best ideas come from group conversations and you're eager to build a "common vision" with a small, brilliant team.
About Guzco
We are a small team of passionate, curious people dedicated to solving hard problems. Our culture is our foundation:
We hire the best and let them work. We give you the freedom, trust, and tools to build end-to-end solutions.
We go deeper. We're critical thinkers who obsess over the details and know that the best solution is rarely the first one.
We are partners, not "resources." We create an atmosphere where everyone talks to everyone. Your voice and your ideas are critical from day one.
We are building the "best infrastructure" for our team to create their best work, with no bureaucracy.
If you are a curious, resilient builder who wants to own a core part of a new product, we would love to talk.
