Senior Machine Learning Engineer (Computer Vision)

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 and provides actionable insights to help businesses in a variety of industries. Join us, as we continue to expand, grow and innovate, and help take Scandit to the next level.

About the role

We are looking for a new colleague to join the Label Capture team and work on creating digital twins of complex labels and identification documents. You will be responsible for evolving our current product by adding new features and optimizing the existing offering. You will work on computer vision and deep learning algorithms to improve information extraction from printed documents beyond state of the art.

If these challenges sound interesting to you, we’d love to hear from you!

What you will do

• Driving and solving semantic document parsing for our customer use cases
• Be involved hands on in all phases of end to end quality improvements (defining and tracking metrics, prototyping, testing, integration of existing models, creation of new models, data collection, analysis, debugging)
• Stay up to date with the latest research in document classification, text recognition and information extraction
• Set direction and identify opportunities to push boundaries of recognition
• Mentoring team members on code quality and engineering practices

Who you are 

You are a senior ML engineer who is passionate about building, shipping, and improving machine learning-powered systems. You have a proven track record of delivering state-of-the-art computer vision systems both on-device and on cloud and firsthand experience bringing them from the prototyping stage to production.

You recognize that software development encompasses both technology and human interaction and feel comfortable working independently or on a team. While you stay on top of the state-of-the-art in CV/ML, you value proven technologies and small increments. You are creative and determined to solve real-world problems by thinking outside the box and making an impact. You thrive on tackling open problems, taking ownership, and effectively overcoming obstacles. You pay attention to details and at the same time are able to take pragmatic shortcuts to reach our goals.

In particular, we are excited to hear from you if the following sounds like you:

• 5+ years of experience solving recognition problems in the industry with a proven track record of launched products
• Fluency in C++ and Python
• Running ML models on mobile devices
• Experience with relevant machine learning frameworks (Pytorch, Jax, Tensorflow)
• Skilled in software design, problem-solving, and debugging
• Experience in computer vision systems extracting information from printed documents or experience with fine-tuning of vision-language models is a plus

What we offer

Here are just some of the reasons why people choose to build their career 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 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

Who We Are

Scandit enables enterprises and consumers to change the way they interact with everyday objects and augment the physical world with real-time data captured by scanning barcodes and recognizing text, objects, and other visual identifiers using smartphones, tablets, wearables, drones and robots.

Scandit’s mobile data capture platform is built on proprietary computer vision, augmented reality, and machine learning technologies. Companies in industries such as retail, transportation & logistics, manufacturing, and healthcare can use Scandit’s technology to create and power mobile apps for crucial enterprise workflows like mobile point of sale, mobile shopping, self-checkout, inventory management, and proof of delivery.

Many of the world’s most innovative and successful companies are benefiting from Scandit’s enterprise-grade mobile data capture platform, including Sephora, Nissan, DHL, and Levi Strauss & Co.

Scandit was founded by a group of researchers from ETH Zurich and MIT. The company is headquartered in Zurich and is also represented in Boston, London, Warsaw, Tokyo and Tampere. Over the years, our company has evolved into a world-class team of mobile image processing, cloud-computing and “Internet of Things” experts from around the globe and has most recently also opened an office in Tampere, Finland.

“Everybody is welcome here” - Is a celebrated component of our DNA.

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 applications will receive consideration for employment without regard to race, colour, nationality, religion, sexual orientation, gender, gender identity, age, physical [dis]ability or length of time spent unemployed.

#LI-MB1

#engineering

The Role

The Lead Researcher in Quantum Machine Learning will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Lead Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Lead Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

Reporting directly to the Director of AI, the Lead Researcher in Quantum Machine Learning plays a pivotal role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Lead Applied Researcher should expect to work in the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Keeping up with state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Assisting in writing patent draft applications
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Lead Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning.

• PhD in computer science, physics, mathematics, electrical engineering, or equivalent subject is required
• Publication record in quantum machine learning, or variational quantum algorithms is required
• Advanced expertise in one or more quantum programming languages (Qiskit, Q#, Pennylane, Cirq, Quipper, Scaffold, tket) is required
• Advanced experience working with variational quantum algorithms for machine learning is required
• Experience working with the classical machine learning algorithms is required
• Computational learning theory expertise is optional
• Expert knowledge in Python is optional
• Expert knowledge in Pytorch and TensorFlow is optional
• Experience in programming for GPUs is optional
• Experience in working with QPUs is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• Proficiency in written and spoken English
• Applicants must have the legal right to live and work in the European Union or Switzerland. Unfortunately, we are unable to offer visa sponsorship for this role.

The Role

The Researcher in Quantum Machine Learning or Quantum Information will be a member of Terra Quantum's AI Applied Research team. This team is at the forefront of conducting both fundamental and applied research in the field of quantum machine learning. Although the Researcher will focus on innovating in quantum machine learning, the role involves translating research findings into practical algorithmic software solutions. The Researcher is expected to closely monitor and analyse scientific and industrial trends and needs across short-term, mid-term, and long-term horizons, specifically within the quantum technology and machine learning sectors, to guide the development and implementation of hybrid quantum-classical algorithms.

The Researcher in Quantum Machine Learning plays a role in driving excellence within their team. They are not only detail-oriented but also possess a remarkable capacity for enthusiasm. By demonstrating commitment and passion for the mission, they inspire their team members to contribute to making quantum technologies widely accessible and to effect positive change globally.

The Responsibilities

The Applied Researcher should expect to work in one and supporting in the other areas of the following Quantum Machine Learning Team activities.

• Fundamental research in quantum machine learning
  • Developing quantum machine learning algorithms for different data processing tasks, e.g. time series, routing and planning, image, graph data, or natural language processing
  • Exploring state-of-the-art approaches in quantum machine learning
  • Analysing parametrised quantum circuits in their capacity to learn via a hybrid quantum-classical optimization loop
  • Utilizing metrics of, e.g., the Fisher information matrix, the effective dimension, and Fourier terms accessibility for parametrized quantum circuit ansatz analysis
  • Improving the trainability of parametrised quantum circuits through layerwise batch-entropy regularization and similar techniques
  • Interpreting and explaining quantum machine learning models. Analysing the flow of information through different architectures of quantum neural networks
  • Researching and understanding where the quantum brings the benefit to machine learning
  • Developing data encoding and data processing techniques for different types of quantum computers
  • Writing research papers for scientific journals

• Efficient implementation of quantum machine learning algorithms
  • Executing quantum machine learning algorithms on QPUs, e.g. of QuEra, IonQ, Rigetti, and IBM Q
  • Adjusting and improving implementation of hybrid quantum neural networks for different QPUs
  • Exploring and testing the best ways to hybridize classical machine learning solutions with quantum machine learning
  • Understanding the way to implement an efficient interaction of hardware (CPU, GPU) and software (PyTorch, Pennylane)
  • Assisting in the development of our quantum machine learning SDK
  • Optimising, at least theoretically, the code of hybrid quantum-classical machine learning solutions for faster execution

• Supporting industry projects implementation
  • Bringing in novel ideas based on industry needs in time-series, routing and planning, GenAI, and natural language processing tasks
  • Brainstorming on possibilities of quantum machine learning algorithms applications to our client’s industry problems
  • Working on finding a theoretical or empirical advantage of using hybrid quantum-classical machine learning in industrial problems
  • Assisting in writing applied industry research papers for scientific journals

The Requirements

The Applied Researcher is expected to have several qualifications depending on the area of activity in quantum machine learning or quantum information.

• Currently pursuing or recently completed a degree in computer science, physics, mathematics,
  or equivalent subject
• Experience in one or more quantum programming languages (Qiskit, Pennylane, Cirq, etc.)
• Experience working with variational quantum algorithms for machine learning
• Experience working with classical machine learning algorithms
• Basic knowledge in Python, Pytorch and TensorFlow
• Experience in working with QPUs is optional
• Familiarity with quantum information theory is optional
• Familiarity with Tensor-Network techniques is optional
• Goal-oriented, analytical and the ability to work independently
• Flexible, proactive, and creative with the ability to work in the team
• Highly motivated and resilient with the ability to work interdisciplinary
• Proficiency in written and spoken English
• Applicants must have the legal right to live and work in the European Union or Switzerland. Unfortunately, we are unable to offer visa sponsorship for this role.

Role

As a Senior Research Scientist in the Applied Research team, you conduct applied research and design innovative algorithms in the space of geometric computer vision and image/video/vision-centric foundation models for deployment in industrial robotics at scale. In this role you define processes and develop approaches in Deep Learning that mitigate the risks of hallucinations, improve accuracy and deploy algorithms on industrial robotics workcells to gauge sim-to-real transfer of models. As a Senior Research Scientist at Intrinsic you provide input and guidance for the development and generation of synthetic data and annotations required for training foundation models at scale based on the identification of gaps in the sim-to-real transfer of models. You also work with Research partners and cross-disciplinary engineering teams to ideate new approaches and architectures to bring state of the art foundation models to real-world industrial applications. Constant learning and exploration of unfamiliar technologies is also part of this role. 

How your work moves the mission forward

Your work can play a significant role in democratizing access to advanced AI skills to the average user in robotics and enhance the credibility of deploying AI models at scale in industrial settings. As part of your day-to-day work: 

• You are insightful and innovative, proactively identifying needs and bottlenecks based on customer problems and proposing practical solutions that are deployable in high throughput environments.
• You serve as the nexus between the robotics team and our research partners—collaborating with them, providing engineering insights into research projects, and ideating practical next generation solutions. 
• You serve as an advisor to the engineering and other cross-functional teams providing critical guidance in terms of translating early (AI-powered) research capabilities into robust engineering capabilities in Flowstate (Intrinsic’s software platform for robotics solutions).
• You continually keep abreast of the latest developments in deep learning, AI, machine learning, perception to ensure that Intrinsic can continually keep evolving its advanced capabilities over time.
• Document and develop the prototype fully so it can be shipped to external users. 

In all the above, you will work closely with a sub-team within the R&D division, but will also frequently collaborate with other engineering, and product groups within Intrinsic.

Skills you will need to be successful

• Master’s or PhD degree in Computer Science, Robotics, or a related technical field with a high degree of passion for robotics and foundation models and a strong inclination towards accelerating the deployment of such models in practical industrial manufacturing applications.
• 3 years of experience in robotics software development or research (work on your PhD can count towards this).
• Proficiency programming in Python and at least one of the ML/DL frameworks such as PyTorch, Jax, and/or Tensorflow.
• Knowledge of robotics perception or manipulation algorithms.
• Experience working with robot hardware or simulation environments.
• A team player with a product mindset, a willingness to learn, and apply oneself to delivering value in practical real-world problems.

Skills that will differentiate your candidacy

• Advanced degree (Master's or PhD) in Robotics, Computer Science, or a related technical field.
• Expertise in deep learning, including transformer-based approaches for robotics, and other areas such as Learning from Demonstrations, Reinforcement learning.
• Proven ability to apply robotics ML/AI research to real-world problems and demonstrate tangible impact.
• Experience generating or utilizing synthetic data for machine learning in robotics.
• Publications in relevant robotics conferences or journals.