Senior Computer Vision Engineer (Action Recognition)

Scandit gives people superpowers. Whether enabling delivery drivers to make quicker deliveries, matching a patient with their medication, or allowing retailers to make store operations more efficient, our technology automates workflows. It provides actionable insights to help businesses in a variety of industries. Join us as we continue to expand, grow, innovate, and help take Scandit to the next level.

We are now building a new generation of technology for retail: a computer vision–driven system that helps retailers to increase their profit margins through better operational insight and automation. As a Senior Computer Vision Engineer, you will play a leading role in taking this product from 0 to 1, owning key technical decisions and delivering production-grade vision capabilities end to end.

You will own the technical direction for action recognition in real‑world retail environments: from defining how we collect and annotate video data, to prototyping and evaluating models, to iterating on precision, recall and end-to-end alert latency under production‑like conditions. You'll work closely with product, data, and engineering stakeholders to validate the riskiest assumptions quickly and cost‑effectively.

If you enjoy turning ambiguous, high‑impact problems into working ML products – and have experience building commercial computer vision systems for action recognition, autonomous checkout, worker safety, or similar applications – we'd love to talk.

As a Senior Computer Vision Engineer, you will be responsible for leading the technical discovery and early development of Scandit's new solution – from validating the riskiest assumptions to building and iterating on the first end-to-end prototypes. You will work closely with product, other engineering teams, and external stakeholders to turn real-world video data into robust, privacy-aware computer vision models that can operate in live retail environments. This role connects cutting-edge CV/ML research with real-world product constraints – requiring both deep technical expertise and a pragmatic, experimental approach to building production-ready systems under ambiguity and time pressure.

• Defining and prioritizing technical experiments to validate key risks (e.g. precision, recall, latency) for computer vision models in retail stores
• Designing and implementing end-to-end CV/ML pipelines on real-world video data, from data ingestion and preprocessing through model training, evaluation, and deployment
• Setting up and refining the human annotation workflow to create high-quality training and test datasets from video streams
• Collaborating with product managers and engineering leaders to clarify requirements, align on milestones, and translate business needs into technical roadmaps
• Selecting, adapting, and optimizing state-of-the-art computer vision architectures for suspicious behavior and action detection in unconstrained environments
• Providing technical leadership in the space of video-based computer vision applications, helping shape best practices and future hiring for the team

We are looking for a senior computer vision / machine learning engineer who has successfully built and shipped real-world products, ideally involving action recognition on video. You combine deep hands-on technical expertise with a pragmatic, experimental mindset and are comfortable working in a high-ambiguity, 0→1 product environment. You should enjoy collaborating closely with product and engineering stakeholders, shaping the technical direction, and iterating quickly on ideas to validate the riskiest assumptions.

• University degree in Computer Science, Electrical Engineering, Mathematics or a related technical field (advanced degree is a plus but not mandatory)
• Significant hands-on experience in computer vision and machine learning, including designing, training, evaluating and deploying models in production
• Proven track record of building end-to-end CV/ML systems from 0→1 (ideally commercial products rather than purely academic projects)
• Strong expertise with video-based pipelines and human action or behavior recognition (e.g. retail, autonomous checkout or similar domains)
• Solid software engineering skills (e.g. Python and modern ML/CV frameworks) and experience working with real-world, noisy data
• Ability to work in a dynamic, high-uncertainty environment, prioritize experiments, and iterate quickly on technical approaches
• Ability to collaborate effectively with cross-functional teams (e.g. product, backend, data, operations) and communicate complex technical topics clearly in English (B2 level or higher)
• Eligible to work in the hiring location

• Experience with privacy-aware or GDPR-compliant data pipelines, especially for video data
• Background in building or selecting annotation tools and workflows for computer vision training data
• Prior work on retail, autonomous stores using computer vision

Here are just some of the reasons why people choose to build their careers at Scandit:

• A highly skilled team and a fun environment where you can put your enthusiasm for cutting-edge technologies to use
• Hackathons
• Flexible, office, hybrid or home working
• People-first culture
• Global team outings
• Festive/end of year all company celebrations
• Your birthday off
• An attractive individual equity plan in a high growth company
• We are certified as a “Great Place to Work” in 7 countries!
• Excellent office infrastructure, optimized for hybrid working in Zurich, Warsaw, Tampere, and London.
• Excellent support for remote work across Switzerland, Finland, Poland, UK, Italy and Germany
• Specific benefits related to the location you are joining

At Scandit we strive to create an inclusive environment that empowers our employees. We believe that our products and services benefit from our diverse backgrounds and experiences and are proud to be a safe space for all. All qualified applicants will receive consideration for employment without regard to race, color, nationality, religion, sexual orientation, gender, gender identity, age, physical [dis]ability or length of time spent unemployed.

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