Senior Manufacturing Data Science & Machine Vision Engineer

Ōura•San Francisco, CA

11h•$172,550 - $203,000•Hybrid

About The Position

Oura is seeking a Senior Manufacturing Data Science & Machine Vision Engineer to build an intelligence layer for global manufacturing. This hands-on, supplier-facing role integrates factory images, machine-learning models, equipment signals, MES data, test results, and material genealogy into reliable production capabilities. The engineer will deploy machine-vision and machine-learning solutions to improve inspection, yield, process control, root-cause analysis, and traceability across contract manufacturers and suppliers. The role covers manufacturing data foundations, factory machine-vision systems, automated manufacturing analysis, and technical deployment strategy. It includes leading data, vision, and analytics workstreams through NPI phases from EVT and DVT through mass production and sustaining, and collaborating with cross-functional teams, contract manufacturers, and equipment vendors. The role is hybrid and based out of Oura’s San Francisco office, with regular travel to manufacturing sites in Asia and other supplier locations.

Requirements

5 to 8 years of relevant experience in manufacturing software, industrial data science, machine vision, automation, test engineering, supplier development, or an equivalent combination of experience.
Bachelor’s or master’s degree in computer science, electrical engineering, mechanical engineering, manufacturing engineering, robotics, data science, or a related field.
Experience deploying software, analytics, or machine-learning solutions in a factory, lab, warehouse, or other production environment.
Strong Python programming skills and experience with data-processing and machine-learning libraries such as pandas, NumPy, scikit-learn, PyTorch, or TensorFlow.
Experience with computer vision and image-processing techniques, such as OpenCV, image classification, object detection, segmentation, OCR/OCV, anomaly detection, or metrology.
Understanding of MES, manufacturing test systems, equipment interfaces, PLC or industrial-control data, and unit-level traceability concepts.
Experience with statistical methods, design of experiments, process capability, SPC, measurement-system analysis, or manufacturing-quality analysis.
Demonstrated ability to work directly with contract manufacturers, suppliers, equipment vendors, and cross-functional engineering teams.
Willingness and ability to travel regularly to manufacturing sites in Asia and other supplier locations.

Nice To Haves

Experience integrating cameras, lighting, lenses, barcode readers, robots, PLCs, motion systems, or automated inspection equipment.
Experience with digital manufacturing, discrete-event simulation, digital twins, or factory capacity modeling.
Experience with electronics, battery, precision assembly, SMT, molding, coatings, laser processing, or other high-volume consumer-electronics manufacturing processes.
Strong technical writing and stakeholder-management skills, including the ability to influence without direct authority.

Responsibilities

Architect and integrate end-to-end manufacturing data systems, including MES, testers, and cloud systems, across the supplier network.
Standardize data models and improve traceability from raw components through final product shipment.
Establish data-quality checks, validation rules, and monitoring frameworks.
Identify and implement machine-vision and image-based inspection opportunities in factory operations.
Design and deploy vision systems, including hardware specifications for optics, cameras, and lighting, and classical and deep-learning algorithms.
Establish data-labeling workflows and model-validation frameworks that convert vision insights into manufacturing decisions to optimize yield and quality.
Build and deploy automated analysis engines and ML/vision models to detect process drift, defects, and anomalies in production.
Implement automated dashboards and alerts that correlate images with process parameters and test data to accelerate root-cause analysis and prevent yield loss.
Develop reusable analytics libraries and tools to scale data-driven insights across Oura manufacturing programs and its partner network.
Explore digital-twin and simulation approaches to model manufacturing scenarios, line capacity, and process flows.
Lead technical execution of data, machine-vision, and analytics workstreams through NPI phases, from EVT and DVT to mass production and sustaining.
Collaborate with cross-functional teams, contract manufacturers, and equipment vendors to define technical requirements, acceptance criteria, and production-system deployment and maintenance standards.
Identify and prioritize industrial AI, vision, and connected-manufacturing opportunities to address production challenges and improve quality, yield, and cycle time.
Define and communicate the long-term manufacturing intelligence roadmap, establish reusable data and vision standards, and influence stakeholders on technical investments.