Senior Engineer, Machine Learning, Vision

Rockwell Automation is a global technology leader focused on helping the world’s manufacturers be more productive, sustainable, and agile. With more than 28,000 employees who make the world better every day, we know we have something special. Behind our customers - amazing companies that help feed the world, provide life-saving medicine on a global scale, and focus on clean water and green mobility - our people are energized problem solvers that take pride in how the work we do changes the world for the better.

We welcome all makers, forward thinkers, and problem solvers who are looking for a place to do their best work. And if that’s you we would love to have you join us!

Position Summary:

Rockwell Automation is building FT Analytics Vision machine vision and visual inspection into the Automation Control Software portfolio, alongside Studio 5000 Logix Designer, Factory Talk Optix, and Data Mosaix. You will implement the machine learning capabilities customers actually touch - the self-learning inspection tools, color and measurement tools, and the training workflows that let a plant engineer teach the system a new defect without a data scientist in the room.

This is an implementation-heavy role with real ownership. You will take a modeling approach from prototype to a shipped, monitored feature, and you will own its accuracy and latency in production.

You will build in an AI-first engineering environment - coding agents running inside a harness we own, evals gating AI-generated changes, Git Hub Copilot Enterprise and Claude in the daily loop, and MCP-based tooling that lets agents reach real build, test, and telemetry systems under human-in-the-loop review. On this team that extends into the model workflow itself: agents scaffold experiments and triage dataset failures, and an accuracy regression suite gates a model change the same way a test suite gates a code change.

• Implement and ship machine learning capabilities in the product, from model training pipeline through the user-facing configuration workflow.
• Build and maintain the training, labeling, and dataset management pipelines the product and the field depend on.
• Develop evaluation suites and accuracy regression tests that gate model changes before release.
• Tune models and inference pipelines to meet latency and throughput targets on edge hardware.
• Debug model behavior against real customer data and turn field failures into dataset and evaluation improvements.
• Participate in design, code, and model reviews, and contribute to the team's ML engineering practices.

• Bachelor's Degree or Equivalent Years of Relevant Work Experience

• Typically requires 5+ years of related experience in a software product development environment.
• Bachelor's or advanced degree in Computer Science, Electrical Engineering, or a related technical discipline.
• Depth in PyTorch or Tensor Flow and in modern computer vision architectures.
• Experience with edge inference and model optimization - ONNX, TensorRT, quantization.
• Experience building dataset and labeling pipelines, including handling class imbalance and label noise.
• Familiarity with evaluation-driven workflows: offline metrics, regression suites, and production monitoring.
• Exposure to industrial automation, machine vision, or manufacturing quality systems.
• Hands-on experience building and training machine learning models in Python
• Experience deploying a model into a production system and supporting it afterward

• Health Insurance including Medical, Dental and Vision
• 401k
• Paid Time off
• Parental and Caregiver Leave
• Flexible Work Schedule where you will work with your manager to enjoy a work schedule that can be…