Applied Scientist (Computer Vision)

Location: Reading, England, United Kingdom

Department: Materials Science & Device Engineering

Workplace: hybrid

Employment Type: full

Description

Applied Scientist – Computer Vision

At OQC, we aren’t just theorising about the future; we’re building it. Born from a philosophy of bold innovation, we’ve successfully transitioned quantum computing from an academic dream into a commercial reality. The most exciting thing is that we’re just getting started and we’ve recently closed our £260 million Series C funding round – the largest fundraise ever completed by a quantum computing company in Europe.

The Purpose

As an Applied Scientist specialising in Computer Vision, you'll turn complex scientific imagery into quantitative insights that help us build better quantum processors. Combining classical image processing with deep learning, you'll develop end-to-end analysis capabilities that transform microscopy and metrology data into reliable measurements for process monitoring, scientific investigation and decision-making.

The Role

Working across our nanofabrication and materials teams, you’ll contribute to the development of computer vision and scientific image-analysis solutions, taking ownership of the development, validation and quality of the solutions you deliver. 

You'll tackle problems spanning microscopic defect detection, segmentation and critical-dimension measurement, working with imagery from technologies including optical microscopy, SEM and AFM. You'll collaborate closely with scientists and domain experts to understand the underlying measurement challenges, while partnering with Platform Engineering to take validated pipelines into production.

What You'll Be Working On

• Design, build and maintain computer vision and image-analysis pipelines that transform scientific imagery into reliable quantitative outputs.
• Develop and apply classical and deep-learning approaches for problems such as defect classification and detection, segmentation, alignment and quantitative measurement.
• Validate image-analysis methods using appropriate ground truth, performance metrics and error analysis, and understand where and why algorithms fail.
• Work with fabrication, materials and metrology specialists to translate scientific questions and measurement requirements into practical analysis solutions.
• Develop maintainable, reusable software within shared scientific codebases, including image-analysis tools, automated analysis pipelines and broader scientific software capabilities. 
• Work with fabrication, materials and metrology specialists to translate scientific questions and measurement requirements into practical analysis solutions. 

What We're Looking For

• Experience with classical computer vision and image processing, including segmentation, morphology, feature/edge detection, registration/alignment and image statistics.
• Experience extracting quantitative measurements from scientific, microscopy or metrology imagery, with an understanding of calibration, resolution, noise, artefacts and measurement uncertainty.
• Experience applying deep learning to computer vision problems, such as classification, detection or semantic/instance segmentation. 
• Strong Python-based scientific computing and ML experience using tools such as NumPy, SciPy, OpenCV/Scikit-image, PyTorch/TensorFlow and Pandas.
• Experience developing maintainable, tested and reusable Python software and automated analysis pipelines, ideally within collaborative codebases. 
• Understanding of software engineering practices for robust scientific tooling, including testing, version control and code review. 
• Understanding of model and algorithm validation, including ground truth, quantitative metrics, error analysis, reproducibility and failure modes.
• Strong scientific problem-solving and communication skills, with the ability to work with domain experts to understand requirements and turn them into effective solutions.
• A methodical and detail-oriented approach to your work, with a commitment to clear documentation and reproducible analysis. 

The 'Nice-to-Haves'

• Experience working in semiconductor, nanofabrication or a related scientific or engineering environment. 
• Exposure to software engineering best practices for robust scientific tooling, such as testing, CI/CD and static analysis.
• Experience with Docker, Kubernetes or other containerisation and orchestration technologies.
• Familiarity with cloud infrastructure.

Why Join OQC

You will join a world-class team at the forefront of the next computational era. We offer a culture of bold innovation, the chance to work with unique lab infrastructure, and the opportunity to see your work redefine the limits of computation.

Learn more about our benefits and positive work culture here: https://oqc.tech/company/careers-at-oqc/