Senior Engineer, Machine Learning, Vision
About The Position
Rockwell Automation is building FT Analytics Vision machine vision and visual inspection into the Automation Control Software portfolio, alongside Studio 5000 Logix Designer, FactoryTalk 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, GitHub 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.
Requirements
- Bachelor's Degree or Equivalent Years of Relevant Work Experience
- Hands-on experience building and training machine learning models in Python
- Experience deploying a model into a production system and supporting it afterward
Nice To Haves
- 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 TensorFlow 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.
Responsibilities
- 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.
Benefits
- Health Insurance including Medical, Dental and Vision
- 401k
- Paid Time off
- Parental and Caregiver Leave
- Flexible Work Schedule
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
