Duration: Minimum 6 months; ideally 9–12 months, depending on the candidate’s experience

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 Internship

We are offering a research-focused internship aimed at advancing machine learning methods for complex visual understanding tasks. The project centers on deep learning architectures for image-to-sequence modelling, such as Transformers, attention mechanisms, and modern sequence and representation-learning frameworks, to address challenging and highly structured computer vision problems. This project contributes to long-term research efforts aimed at achieving even higher performance, robustness, and generalization in large-scale visual applications.

What you will do

You will work closely with experienced ML researchers and engineers on cutting-edge research at the intersection of computer vision and sequence modeling. Your work will include:

• Designing and experimenting with new ML architectures for structured visual data.
• Evaluating alternative modeling paradigms (e.g., encoder–decoder, hybrid Transformer models, sequence-based representations).
• Investigating techniques for improving robustness, generalization, and multi-view reasoning.
• Running systematic experiments, ablations, and error analyses to validate research hypotheses.

This project provides opportunities for novel model design, extensive experimentation, and scholarly research. You will contribute to long-term innovation in our technology, with potential real-world impact for millions of users. An ideal position for experienced master’s students, PhD collaborations, or candidates preparing for a research career in industry or academia.

Who you are

MSc or PhD student in Computer Science, Machine Learning, Artificial Intelligence, or a related field with a strong research focus. Candidates should have a solid foundation in machine learning theory, neural networks, and computer vision.

Essential Skills:

• Proficiency in Python and deep learning frameworks such as PyTorch.
• Practical experience designing, training, and evaluating neural networks, including CNNs and Transformer-based architectures.
• Strong analytical and problem-solving abilities, with the capability to interpret experimental results and iterate effectively.
• Familiarity with research best practices, including reproducibility, controlled experiments, and ablation studies.

Desirable Skills:

• Prior research experience in computer vision, pattern recognition, sequence modeling, or image-to-sequence architectures.
• Experience training large-scale models or working with foundation-style architectures.
• Contributions to publications, preprints, or open-source machine learning projects.

Strong communication skills and the ability to work independently in a research-oriented environment.

What We Offer

• We are certified as a “Great Place to Work” in 10 countries!
• A highly skilled team and a fun environment where you can put your enthusiasm for computer vision challenges and cutting-edge technologies to use
• Hackathons, summer parties, company outings and other regular events
• Office in the city center of Zurich

Who We Are

Could your code give 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. This means we have no shortage of technical challenges for engineers like you. Join us, as we continue to expand, grow and innovate, and help take Scandit to the next level.

“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

#Hybrid

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.

About the Internship

We are looking for students with a passion for computer vision or machine learning to join one of the teams in Zürich for four to six months. You will be a part of product development and you will help us improve and extend algorithms and their automated testing infrastructure.

Scandit offers an excellent opportunity to apply the theory from your studies while gaining hands-on experience with industry best practices in a international team.

What You Will Do

We have many interesting projects to offer. Depending on your background and interests, you could work on one of the following projects:

• Improve barcode scanning algorithms in difficult scenarios such as surfaces with deformations, glare or reflections
• Enhance the real-time visual tracking and pose estimation system of our solutions through the integration of additional sensor measurements and cutting-edge machine learning algorithms
• Enhance our object and context detection systems by developing, refining, and integrating state-of-the-art machine learning algorithms tailored to our specific domain
• Enhance our real-time visualization system to analyze and develop our computer vision algorithms
• Reduce manual steps in our image annotation tools with smart algorithms
• Develop image classification, matching and/or reconstruction features in order to improve the algorithms for understanding the current state of shelves and products in supermarkets

Our Tech Stack

• Computer vision algorithms: C++17, CMake
• Machine learning training and tooling, cloud processing: Python, Pytorch
• Acceleration: SIMD, Vulkan, CoreML

Who You Are

• Highly motivated student, interested in an internship of four months or longer
• Enrolled in or completed a bachelor’s or master’s program in computer science, information technology, robotics or a related field
• Solid knowledge of data structures and experience in object-oriented programming
• You have taken courses in computer vision, image processing and/or machine learning
• Ideally you already had some exposure to solving a computer vision problem in a semester project or thesis
• If you are interested in working on the core image recognition algorithms, solid C++ programming skills will be needed
• Python programming knowledge is a plus

What We Offer

• We are certified as a “Great Place to Work” in 7 countries!
• A highly skilled team and a fun environment where you can put your enthusiasm for computer vision challenges and cutting-edge technologies to use
• Hackathons, summer parties, company outings and other regular events
• Office in the city of Zürich (Hardbrücke)

Imagine the What. Build the How.

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. 

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

Duration: Minimum 6 months; ideally 9–12 months, depending on the candidate’s experience

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 Internship

We are offering a research-focused internship aimed at advancing machine learning methods for complex visual understanding tasks. The project centers on deep learning architectures for image-to-sequence modelling, such as Transformers, attention mechanisms, and modern sequence and representation-learning frameworks, to address challenging and highly structured computer vision problems. This project contributes to long-term research efforts aimed at achieving even higher performance, robustness, and generalization in large-scale visual applications.

What you will do

You will work closely with experienced ML researchers and engineers on cutting-edge research at the intersection of computer vision and sequence modeling. Your work will include:

• Designing and experimenting with new ML architectures for structured visual data.
• Evaluating alternative modeling paradigms (e.g., encoder–decoder, hybrid Transformer models, sequence-based representations).
• Investigating techniques for improving robustness, generalization, and multi-view reasoning.
• Running systematic experiments, ablations, and error analyses to validate research hypotheses.

This project provides opportunities for novel model design, extensive experimentation, and scholarly research. You will contribute to long-term innovation in our technology, with potential real-world impact for millions of users. An ideal position for experienced master’s students, PhD collaborations, or candidates preparing for a research career in industry or academia.

Who you are

MSc or PhD student in Computer Science, Machine Learning, Artificial Intelligence, or a related field with a strong research focus. Candidates should have a solid foundation in machine learning theory, neural networks, and computer vision.

Essential Skills:

• Proficiency in Python and deep learning frameworks such as PyTorch.
• Practical experience designing, training, and evaluating neural networks, including CNNs and Transformer-based architectures.
• Strong analytical and problem-solving abilities, with the capability to interpret experimental results and iterate effectively.
• Familiarity with research best practices, including reproducibility, controlled experiments, and ablation studies.

Desirable Skills:

• Prior research experience in computer vision, pattern recognition, sequence modeling, or image-to-sequence architectures.
• Experience training large-scale models or working with foundation-style architectures.
• Contributions to publications, preprints, or open-source machine learning projects.

Strong communication skills and the ability to work independently in a research-oriented environment.

What We Offer

• We are certified as a “Great Place to Work” in 10 countries!
• A highly skilled team and a fun environment where you can put your enthusiasm for computer vision challenges and cutting-edge technologies to use
• Hackathons, summer parties, company outings and other regular events
• Office in the city center of Tampere

Who We Are

Could your code give 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. This means we have no shortage of technical challenges for engineers like you. Join us, as we continue to expand, grow and innovate, and help take Scandit to the next level.

“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.

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.

At Inceptive, you will drive forward development that could help billions of people. To accomplish this, you will be part of a collaborative, antedisciplinary team building our biological software.

The design space of biomolecules is unimaginably vast — far beyond what can be explored experimentally. Yet within this space lie molecules with properties essential for new medicines. Our machine learning models learn to design therapeutic biomolecules with specific, desirable functions.

We advance the state of the art in molecular design by training large-scale foundation models and developing cutting edge generative approaches. The models learn from diverse heterogeneous datasets and are refined through focused fine-tuning and feedback from experiments. Key to progress is a team that combines exceptional machine learning expertise with thorough domain understanding.

You will collaborate closely with other machine learning researchers and engineers, as well as computational and experimental biologists, to advance these models and translate their capabilities into real therapeutic designs.

Your Mission, should you choose to accept it

• Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
• Develop, implement, train, and iteratively improve state-of-the-art models for biomolecule design
• Analyze, visualize, and communicate results to support team efforts in improving models and data
• Create, deploy, and refine tools for efficient, reliable machine learning experimentation and production
• Work with biologists to collect data for the training and evaluation of generative models of biomolecules
• Provide mentorship and technical direction to team members as appropriate

Qualifications

• 3+ years of hands-on experience developing ML models
• Demonstrated track record of implementing, training, improving advanced machine learning models
• Highly capable programmer fluent in Python ecosystem and PyTorch or similar deep learning framework
• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET
• Readiness to travel several times a year for company retreats and business events
• We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations

Preferred technical skills

• PhD in AI/ML, computer science, computational biology, physics, or a related field, or equivalent practical experience (e.g., industry experience, research, or advanced technical expertise)
• Strong skills in designing, executing, and documenting machine learning experiments
• Practical experience with modern generative models
• Strong software engineering skills, in particular for data processing, evaluation of ML models, compute cluster orchestration
• Experience with large-scale model training, foundation models, model parallelism, multi-node training
• Experience with bio sequence data and datasets — various genomic and protein data, sequencing, functional assays, etc
• Knowledge of biochemistry, molecular/cell biology, and drug development

Compensation

$200K – $275K + Bonus + Equity

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.